Urban max = 40 °C Rural max = 27°C UHI = (40-27) °C = 13°C

**Figure 17.** Figure 17. Tehran surface temperature map

The UHI intensity of Tehran is:

Urban max = 39 °C Rural max = 27°C UHI = (39-27) °C = 12°C

Therefore, daytime Tehran surface UHI shows 12°C of differences between urban and rural areas.

The UHI intensity of 6 urban district is:

Urban max = 40 °C Rural max = 27°C UHI = (40-27) °C = 13°C

298 Air Pollution – A Comprehensive Perspective

**7.1. Urban heat island measurement**

instantaneous observation during the daytime.

**Figure 17.** Figure 17. Tehran surface temperature map

The UHI intensity of Tehran is:

Urban max = 39 °C Rural max = 27°C UHI = (39-27) °C = 12°C

areas.

The clear observation is the surface temperature of Tehran in 18 July 2000 (Figure 17). The warm region where is represented by red and yellow colour, is mostly located in the central, western and southern part of Tehran where CBD, industrial area and airport are located respectively. On the other hand, northern part of Tehran is relatively cool with green colour. This is due to the concentration of greenery and water bodies as well as less impact from the densely placed urban developments. The contrast between urban and rural areas hints at the prevalence of the UHI effect in Tehran, although the satellite image only provides the

Therefore, daytime Tehran surface UHI shows 12°C of differences between urban and rural

**7. Discussion of results**

*7.1.1. Satellite image*

Daytime UHI shows a 13°C of difference between urban and rural area. In fact, UHI intensity of 6 urban district is 1°C higher than Tehran.

Study area (6 urban district) image reveals some spots with either high or low surface temperature. As shown in Figure 18, Region 1 represents some of governmental organization, such as energy organization and some commercial and residential land uses. They experienced the highest temperature during daytime especially in the north and west parts of the region mainly because of lack of extensive landscape and being close to the two main highways (Hemat in north and Chamran in west) as well. Similarly, higher temperature was observed in eastern north and east parts of the Region 2. This is also reasonable since the exposed runway absorbs a lot of incident solar radiation during the daytime and incurs high surface temperature. It is due to the bus terminal station located in the Abassabad lands as well. Region 3 represents the most crowded area with higher traffic congestion, which the majority of commercial land uses are located in this region. Region 5 and 6 are close to the CBD of Tehran and neighbour with the Enghelab street, the most crowded street, but the existing of one of the biggest parks of Tehran, Laleh park, in Region 5 was able to reduce the higher temperature partly. Region 4 also represents some of the commercial and residential land uses. Furthermore, as shown in Figure 18, the areas around the main axis of the district which separates the regions of 1, 3 and 5 from regions of 2, 4 and 6, have lower urban temperature which it can be mainly due to the trees axis in the Vali-e-Asr street. This axis is not the worst scenarios, however, some red spots can be seen in this area. Therefore, the worst scenarios have been occurred in eastern north, western north and also some areas in west of the district. These all can be due to the lack of vegetation covers and low albedo materials and higher density of population and production of anthropogenic heat.

#### *7.1.2. Historical weather data*

The investigation of a 25 years period of urban (Mehrabad station) and rural (Karaj station) temperature in Tehran makes clear the temperature difference between these two areas. The selection of these two stations is because of the long records and validity. Mehrabad station is located within the west region of Tehran which airport situated there. In Karaj station, the major land use is agriculture. At Mehrabad and Karaj stations, the annual maximum, mean and minimum dry-bulb temperatures indicate a slow upward trend towards warming or cooling during the period 1985-2009 (Figure 19 and 20).

An exploration of the mean temperature trends of Mehrabad (urban) and Karaj (rural) as well as temperature differences between the two locations was made. The results show that (Figure 21) for the first five years temperature difference increased from 1.9°C to 3.6°C. This was an increase of 1.7°C for the period. It dropped by 1.7°C in 1991. Over the next 12 years

there were fluctuations. It started increasing from 1.7°C in 2003 to 3.2°C in 2004 and it reached to peak on 2005 before the temperature difference decreased by about 3.6°C to around 2.6°C in 2009. The highest temperature differences is 3.6°C occurred in 1989, 1990 and 2005, while the lowest one with 1.5°C occurred in 1993, 1995 and 1999. It means that from 2003 forward there is higher intensity of UHI.

Mitigating Urban Heat Island Effects in Tehran Metropolitan Area 301

**Figure 20.** Analysis of the past 25 years' weather data at Karaj station

**Figure 21.** Mehrabad-Karaj mean temperature differences

Field measurements are used for measuring the air temperature of current situation of the area. In this way, it was chosen 31 points in three parts of the district. In fact, three ways were traversed, two narrow strips around (from point number 1 to 11 and 12 to 21) and the central part of the district (from point number 22 to 31), to cover whole areas of the district (Figure 22). Since there were only two hours with higher radiation intensity to measure points, this study has selected three consecutive days to measure them exactly in these two hours. It investigated the correlation between temperatures and different land uses in

*7.1.3. Mobile survey*

current condition of 6 urban district.

**Figure 18.** 6 urban district's surface temperature map

**Figure 19.** Analysis of the past 25 years' weather data at Mehrabad station

**Figure 20.** Analysis of the past 25 years' weather data at Karaj station

**Figure 21.** Mehrabad-Karaj mean temperature differences

#### *7.1.3. Mobile survey*

300 Air Pollution – A Comprehensive Perspective

from 2003 forward there is higher intensity of UHI.

**Figure 18.** 6 urban district's surface temperature map

**Figure 19.** Analysis of the past 25 years' weather data at Mehrabad station

there were fluctuations. It started increasing from 1.7°C in 2003 to 3.2°C in 2004 and it reached to peak on 2005 before the temperature difference decreased by about 3.6°C to around 2.6°C in 2009. The highest temperature differences is 3.6°C occurred in 1989, 1990 and 2005, while the lowest one with 1.5°C occurred in 1993, 1995 and 1999. It means that

> Field measurements are used for measuring the air temperature of current situation of the area. In this way, it was chosen 31 points in three parts of the district. In fact, three ways were traversed, two narrow strips around (from point number 1 to 11 and 12 to 21) and the central part of the district (from point number 22 to 31), to cover whole areas of the district (Figure 22). Since there were only two hours with higher radiation intensity to measure points, this study has selected three consecutive days to measure them exactly in these two hours. It investigated the correlation between temperatures and different land uses in current condition of 6 urban district.

Mitigating Urban Heat Island Effects in Tehran Metropolitan Area 303

First route running from north to south and then to east (1 to 11), second route from west to east and then to south (12 to 21) and third route which cover central part of the district (22 to 31) running like zigzag movement to cover whole area of the central part passed through quite a number of different land uses (Figure 23). In order to save time and measure the defined points in defined time, bicycle was selected. According to high traffic jam in Tehran reaching to all points in the exact time by car was impossible. Therefore using vehicle equipped with

The maximum air temperature, 42 °C, was observed in the route number 1 in industrial, commercial and public services. The lowest temperature, 38.5 °C, was observed in residential area (Figure 24). In route number 2, the maximum temperature was also 42°C in industrial and public services and the lowest one was 34°C in park (Figure 25). In route number 3, the highest temperature was 40.5 °C in industrial area and the lowest one was 30

13:00 13:11 13:21 13:36 13:48 13:56 14:10 14:22 14:36 14:49 15:00

(Hospital)

Industrial Tehran University

**Time and Land Uses**

Commercial Commercial-Residential (Mix use)

Commercial Industrial Commercial-

Industrial Park Public Service

Residential (Mix use)

(Church)

observation tube which can automatically record ambient temperature was difficult.

**Figure 24.** Maximum air temperature in different land uses in route number 1

Parking Residential Commercial Public Service

**Figure 25.** Maximum air temperature in different land uses in route number 2

Public Service (Road Organization)

As seen routes number 1 and 2 have the same highest temperature in industrial area and public services but the route number 3 has 1.5 °C less than the other routes. It is due to the location of routes number 1 and 2 and they are located next to the main highways (Hemat, Chamran, Modares and Enghelab highways) which have the highest traffic jam, air pollution, production of anthropogenic heat and last but not least the lack of vegetation

13:00 13:12 13:26 13:32 13:49 14:06 14:20 14:34 14:48 15:00

**Time and Land Uses**

Public Service (Naja Organization)

°C in park (Figure 26).

**Temperature (°C)**

**Temperature (°C)**

Public Service (Geophysics Organization)

Public Service (Communication Organization)

Parking Residential Commercial-

Residential (Mix use)

**Figure 22.** Selection of 31 points in 6 urban district with 1 Km distance for mobile survey

**Figure 23.** Three routes selected for measuring air temperature in 6 urban district

First route running from north to south and then to east (1 to 11), second route from west to east and then to south (12 to 21) and third route which cover central part of the district (22 to 31) running like zigzag movement to cover whole area of the central part passed through quite a number of different land uses (Figure 23). In order to save time and measure the defined points in defined time, bicycle was selected. According to high traffic jam in Tehran reaching to all points in the exact time by car was impossible. Therefore using vehicle equipped with observation tube which can automatically record ambient temperature was difficult.

302 Air Pollution – A Comprehensive Perspective

**Figure 22.** Selection of 31 points in 6 urban district with 1 Km distance for mobile survey

**Figure 23.** Three routes selected for measuring air temperature in 6 urban district

The maximum air temperature, 42 °C, was observed in the route number 1 in industrial, commercial and public services. The lowest temperature, 38.5 °C, was observed in residential area (Figure 24). In route number 2, the maximum temperature was also 42°C in industrial and public services and the lowest one was 34°C in park (Figure 25). In route number 3, the highest temperature was 40.5 °C in industrial area and the lowest one was 30 °C in park (Figure 26).

**Figure 24.** Maximum air temperature in different land uses in route number 1

**Figure 25.** Maximum air temperature in different land uses in route number 2

As seen routes number 1 and 2 have the same highest temperature in industrial area and public services but the route number 3 has 1.5 °C less than the other routes. It is due to the location of routes number 1 and 2 and they are located next to the main highways (Hemat, Chamran, Modares and Enghelab highways) which have the highest traffic jam, air pollution, production of anthropogenic heat and last but not least the lack of vegetation covers which can ventilate air and using low albedo and non-reflective materials. The industrial areas generally have low-rise buildings and the high temperature recorded in these areas is related to the extensive usage of metal roofing in the buildings. The high temperature of commercial and public services buildings is related to use concrete and dark stones (Figure 27) that absorb a huge part of the solar radiation incident on it and later release it to the atmosphere. In addition, calculation of averaging the temperature in every route shows the highest mean temperature in route number 1 with 41 °C, route number 2 with 39.6 °C and route number 3 with 38.25°C respectively (Table 1). From the results it is observed that the daytime temperature seemed to be dominated more by the solar radiation component rather than by the reradiated temperature, which is the main cause of daytime UHI. The average of observations obtained during daytime in three days in 2009 shows that the temperature is 2.2 °C higher than the average of temperature derived from satellite image in 2000. It means that there were more constructions in these 9 years and made the condition much more worse.

Mitigating Urban Heat Island Effects in Tehran Metropolitan Area 305

with satellite image results shows that west, north-west and east parts of the district in satellite image (2000) have higher intensity of UHI, while mobile survey (2009) shows the

Figure 31 shows the correlation between GIS and mobile survey. P-value, which shows 0.05, is significant difference between GIS and mobile survey. The Pearson correlation which is 0.67 shows that the difference is reliable. This means the reading between these two sources

condition much worse.

is complement each other.

**Midnight Temperature (°C)**

Public Service (Geophysics Organization)

Public Service (Communication Organization)

**Figure 27.** Using dark stone, concrete and metal materials

**Figure 28.** Minimum air temperature in different land uses in route number 1

Parking Residential Commercial Public Service

21:00 21:10 21:20 21:37 21:47 21:55 22:10 22:21 22:35 22:48 23:00

(Hospital)

**Time and Land Uses**

Industrial Tehran University Commercial Industrial Commercial-

Residential (Mix use)

**Table 1.** Mean min and max temperatures in three different routes

**Figure 26.** Maximum air temperature in different land uses in route number 3

In urban areas, the night time temperatures varied between 25°C and 35.5°C and it was found that the CBD area was around 7 °C hotter than the locations with greenery (Figure 28, 29 and 30). This also indicates the center of the night time UHI effect which has shifted from the industrial areas during the daytime to the CBD area. The average temperature in every route in night time also shows the highest mean temperature in route number 1 with 30.06 °C, while route number 2 has 29.05°C and route number 3 with 28.90 °C (Table 1). It means that at night time also route number 1 has the highest temperature. Comparing these results with satellite image results shows that west, north-west and east parts of the district in satellite image (2000) have higher intensity of UHI, while mobile survey (2009) shows the condition much worse.

Figure 31 shows the correlation between GIS and mobile survey. P-value, which shows 0.05, is significant difference between GIS and mobile survey. The Pearson correlation which is 0.67 shows that the difference is reliable. This means the reading between these two sources is complement each other.

**Figure 27.** Using dark stone, concrete and metal materials

304 Air Pollution – A Comprehensive Perspective

condition much more worse.

**Temperature (°C)**

Commercial-residential (Mix use)

covers which can ventilate air and using low albedo and non-reflective materials. The industrial areas generally have low-rise buildings and the high temperature recorded in these areas is related to the extensive usage of metal roofing in the buildings. The high temperature of commercial and public services buildings is related to use concrete and dark stones (Figure 27) that absorb a huge part of the solar radiation incident on it and later release it to the atmosphere. In addition, calculation of averaging the temperature in every route shows the highest mean temperature in route number 1 with 41 °C, route number 2 with 39.6 °C and route number 3 with 38.25°C respectively (Table 1). From the results it is observed that the daytime temperature seemed to be dominated more by the solar radiation component rather than by the reradiated temperature, which is the main cause of daytime UHI. The average of observations obtained during daytime in three days in 2009 shows that the temperature is 2.2 °C higher than the average of temperature derived from satellite image in 2000. It means that there were more constructions in these 9 years and made the

Routes Mean max temperature (°C) Mean min temperature (°C)

1 41 30.06 2 39.60 29.05 3 38.25 28.90

**Table 1.** Mean min and max temperatures in three different routes

**Figure 26.** Maximum air temperature in different land uses in route number 3

Parking Commercial Park Industrial Public Service

In urban areas, the night time temperatures varied between 25°C and 35.5°C and it was found that the CBD area was around 7 °C hotter than the locations with greenery (Figure 28, 29 and 30). This also indicates the center of the night time UHI effect which has shifted from the industrial areas during the daytime to the CBD area. The average temperature in every route in night time also shows the highest mean temperature in route number 1 with 30.06 °C, while route number 2 has 29.05°C and route number 3 with 28.90 °C (Table 1). It means that at night time also route number 1 has the highest temperature. Comparing these results

13:00 13:11 13:27 13:39 13:54 14:13 14:24 14:37 14:48 15:00

**Time and Land Uses**

(Transportation Organization)

Hotel Commercial Parking Park

**Figure 28.** Minimum air temperature in different land uses in route number 1

Mitigating Urban Heat Island Effects in Tehran Metropolitan Area 307

The results obtained from modelling based on GIS, 3D and spatial analysis, are described in

This analysis gives visual views of the district that can help to better understand of the site. The 3D of district (Figure 32) shows that the northern part of the district has some ups and downs that contribute to cause an unequal distribution of pollution and provide warm air canopy over this area. As shown in Figure 18, the northern part of the district has the hottest surface temperature that is due to the concentration of the pollutions and cause to form the

To prove the hypotheses spatial analysis based on ArcGIS has been done in order to estimate the area of albedo (lower, medium and higher), vegetation covers and then overlay them. Results obtained from creating albedo (Figure 33) and NDVI grid maps (Figure 34) in

As shown in Table 2, albedo has been classified into three groups including (Figure 33):

According to the Table 3, this range of albedo belongs to concrete (0.10-0.35);

1. Higher albedo with value of 0.170-0.310 occupied 37% of whole area of 6 urban district.

7. Medium albedo with value of 0.140-0.160 occupied 50% of whole area of 6 urban district. According to Table 3, this range of value belongs to asphalt (0.05-0.2) or

8. Lower albedo with value of 0.064-0.130 occupied 13% of whole area of 6 urban district. According to Table 3, this range of value belongs to gravel (0.08-0.18), smooth-surface

As shown in Table 4, the land cover types have been divided into 4 categories including

1. Vegetation with value of 0.0-0.7 from very poor to very high density. This type covers

2. Non-vegetation with value of -0.0- -0.4 including urban area, desert, mountain area and cloud. This type covers 2087.52 hectare, 97.37% of whole area of 6 urban district; 3. Water with value of -0.4- -0.7 constituted 1.7 hectare, 0.08% of whole area; and

4. There are also some land covers that their types were not recognizable which cover 3.21

**7.2. Modelling based on GIS analysis**

following way:

UHI.

*7.2.1. 3D analysis*

*7.2.2. Spatial analysis*

1. Albedo Grid Map

2. NDVI Grid Map

(Figure 34):

corrugated iron (0.10-0.16); and

asphalt (0.07) or black coloured materials.

51.57 hectare, 2.4% of whole area of 6 urban district;

hectare, 0.15% of whole area of 6 urban district.

vector format.

**Figure 29.** Minimum air temperature in different land uses in route number 2

**Figure 30.** Minimum air temperature in different land uses in route number 3

**Figure 31.** Figure 31. Correlation between GIS and mobile survey

#### **7.2. Modelling based on GIS analysis**

The results obtained from modelling based on GIS, 3D and spatial analysis, are described in following way:

#### *7.2.1. 3D analysis*

306 Air Pollution – A Comprehensive Perspective

Parking Residential Commercial-

Residential (Mix use)

**Midnight Temperature (°C)**

Commercialresidential (Mix use)

**Midnight Temperature (°C)**

**Figure 29.** Minimum air temperature in different land uses in route number 2

Public Service (Road Organization)

21:00 21:11 21:25 21:32 21:49 22:05 22:19 22:34 22:47 23:00

**Time and Land Uses**

21:00 21:11 21:25 21:38 21:52 22:13 22:23 22:36 22:47 23:00

**Time and Land Uses**

(Transportation Organization)

Commercial Commercial-Residential (Mix use)

Industrial Park Public Service

Hotel Commercial Parking Park

(Church)

Public Service (Naja Organization)

**Figure 30.** Minimum air temperature in different land uses in route number 3

Parking Commercial Park Industrial Public Service

**Figure 31.** Figure 31. Correlation between GIS and mobile survey

This analysis gives visual views of the district that can help to better understand of the site. The 3D of district (Figure 32) shows that the northern part of the district has some ups and downs that contribute to cause an unequal distribution of pollution and provide warm air canopy over this area. As shown in Figure 18, the northern part of the district has the hottest surface temperature that is due to the concentration of the pollutions and cause to form the UHI.

#### *7.2.2. Spatial analysis*

To prove the hypotheses spatial analysis based on ArcGIS has been done in order to estimate the area of albedo (lower, medium and higher), vegetation covers and then overlay them. Results obtained from creating albedo (Figure 33) and NDVI grid maps (Figure 34) in vector format.

#### 1. Albedo Grid Map

As shown in Table 2, albedo has been classified into three groups including (Figure 33):

#### 2. NDVI Grid Map

As shown in Table 4, the land cover types have been divided into 4 categories including (Figure 34):

Mitigating Urban Heat Island Effects in Tehran Metropolitan Area 309

0.10-0.35 0.20-0.40 0.80 0.55 0.30-0.50 0.20-0.30 0.20 0.30-0.45

0.07 0.10-0.15 0.08-0.18 0.10-0.35 0.10 0.15-0.20 0.10-0.16 0.6-0.7

0.50-0.90 0.20-0.35 0.20-0.15

0.10-0.27 0.15

0.40-0.60 0.30 0.30 0.20-0.30 0.20-0.30 0.15-0.20 0.20 0.10-0.20 0.10-0.15 0.10 0.07-0.10 0.50-0.10 0.05

**Surface Albedo**

Asphalt (fresh 0.05, aged 0.2) 0.05-0.2

Streets

Walls Concrete Brick/Stone Whitewashed stone White marble chips Light-coloured brick Red brick Dark brick and slate Limestone

Roofs Smooth-surface asphalt (weathered) Asphalt Tar and gravel Tile Slate Thatch Corrugated iron Highly reflective roof after weathering

> Paints White, whitewash Red, brown, green Black

> > Urban areas Range Average

Other Light-coloured sand Dry grass Average soil Dry sand Deciduous plants Deciduous forests Cultivated soil Wet sand Coniferous forests Wood (oak) Dark cultivated soils Artificial turf Grass and leaf mulch

**Table 3.** Albedo of typical urban materials and areas [22,3]

**Figure 32.** 3D of 6 urban district

**Table 2.** Albedo classification with related values and area in 6 urban district

**Figure 33.** Albedo grid map of 6 urban district

**Table 3.** Albedo of typical urban materials and areas [22,3]

**Figure 32.** 3D of 6 urban district

**Figure 33.** Albedo grid map of 6 urban district

**Albedo Classification Albedo Value Area (Hectare) Percent of 6 Urban**

2144 100%

Higher albedo 0.170-0.310 792 37% Medium albedo 0.140-0.160 1071 50% Lower albedo 0.064-0.130 280 13%

**Table 2.** Albedo classification with related values and area in 6 urban district

**District Area**

Mitigating Urban Heat Island Effects in Tehran Metropolitan Area 311

2144

**No. Albedo Classification Land Cover Type Area (Hectare)**  1 Higher Albedo Unknown 3.05 2 Higher Albedo Non-Vegetation 739.16 3 Higher Albedo Vegetation 48.80 4 Higher Albedo Water 1.06 5 Medium Albedo Unknown 0.16 6 Medium Albedo Non-Vegetation 1068.08 7 Medium Albedo Vegetation 2.76 8 Medium Albedo Water 0.48 9 Lower Albedo Non-Vegetation 280.28 10 Lower Albedo Water 0.16

**Table 5.** Results of overlaying albedo and NDVI grid map of 6 urban district

**Figure 35.** Overlaying albedo and NDVI map of 6 urban district

albedo and vegetation cover does not exist).

1. Higher albedo with non-vegetation and vegetation covers; 2. Medium albedo with non-vegetation and vegetation covers; and

following groups:

Each number shows the combination of albedo and land cover types. It can be divided into

3. Lower albedo with non-vegetation cover (as seen in Table 5 the combination of lower

**Table 4.** Land cover types with related value and areas in 6 urban district

**Figure 34.** NDVI grid map of 6 urban district

#### **3. Overlaying the albedo and NDVI grid maps**

Results obtained from overlaying the albedo and NDVI grid maps have been shown in Figure 35 and Table 5.

**Table 5.** Results of overlaying albedo and NDVI grid map of 6 urban district

0.0 - 0.1 Very Poor

**Figure 34.** NDVI grid map of 6 urban district

Figure 35 and Table 5.

**3. Overlaying the albedo and NDVI grid maps**

Results obtained from overlaying the albedo and NDVI grid maps have been shown in

0.0 - 0.2 Poor 0.2 - 0.3 Moderate 0.3 - 0.5 High 0.5 - 0.7 Very High

**NDVI value Vegetation density Land cover type Area**

**Table 4.** Land cover types with related value and areas in 6 urban district

0 Unknown Unknown 3.21 0.15% -0.4 - -0.7 Non-Vegetation Water 1.7 0.08%

> Urban area, desert, mountain area and cloud

**(Hectare)**

Vegetation 51.57 2.4%

2087.52 97.37%

2144 100%

**Percent of 6 Urban District Area**

**Figure 35.** Overlaying albedo and NDVI map of 6 urban district

Each number shows the combination of albedo and land cover types. It can be divided into following groups:

Number 2 (Orange colour) shows the combination of higher albedo and non-vegetation with 739.16 hectare area, number 6 (yellow colour) shows the combination of medium albedo and non-vegetation with 1068.08 hectare area, number 9 (green colour) shows the combination of lower albedo and non-vegetation with 280.28 hectare area, which occupied 34.5%, 50% and 13% of 6 urban district respectively. Number 3 (red colour) shows the combination of higher albedo and vegetation with 48.80 hectare area, number 7 (brown colour) shows the combination of medium albedo and vegetation with 2.76 hectare area which occupied 2.27% and 0.12% of 6 urban district respectively. Other numbers are negligible which are not observed in Figure 35. Although number 3 includes high albedo and vegetation cover, it encompass the very low percentage of area which not only it has not impact on mitigation of UHI, but also this value of albedo (with value of 0.17-0.310) with lower reflectivity can increase the UHI intensity. The area of vegetation cover is negligible in comparison with whole area of the district. Number 6 with 50% of whole area of 6 urban district including non-vegetation cover and medium albedo materials (with value of 0.14-0.160) has been widely distributed in the district and provided worse condition for this district. After number 6, number 2 is in the worse condition with 34.5% of whole area of 6 urban district including non-vegetation cover and high albedo materials (with value of 0.17-0.310). Then number 9 with 13% of whole area of 6 urban district and the combination of lower albedo and non-vegetation cover stands in the next rank. It has been widely distributed in region 1 and 2 that the topography of these regions also provided higher UHI intensity. It is also observed in Figure 16 that these regions have higher UHI impacts.

Mitigating Urban Heat Island Effects in Tehran Metropolitan Area 313

2 2 5360825 2 2680412.5 m Vegetation covers

If a third area of 6 urban district is covered with vegetation covers and high albedo

If the amount of vegetation cover and high albedo material increase and cover half of urban

In addition, as Reagan and Acklam [24] calculated, when the reflectivity of the rest of area (5360825 m2) with poorly insulated building is increased from 0.25 to 0.65, the heat gains through the roof are reduced by half. It means that the albedo values mentioned in Table 2 is higher albedo in current classification of 6 urban district and it does not have higher

In comparison with the UN indicator (20-25 m2), it is 8.5 m2 less.

2 2

In comparison with the UN indicator (20-25 m2), it is still 5 m2 less.

3573883.33m Higher albedo materials

area, the effect of UHI can be achieved zero which is the ideal condition, therefore:

5360825 m Higher albedo materials

7147766.67 2 3573883.33 m Vegetation covers

Therefore, per capita of vegetation cover is:

Therefore, this hypothesis also is not applicable.

2 2 10721650 2 5360825m Vegetation covers

In comparison with the UN indicator (20-25 m2), it is acceptable.

Area of 6 urban district = 21443300 m2

Therefore, per capita of vegetation cover is:

Therefore, this hypothesis is applicable.

Therefore, per capita of vegetation cover is:

Therefore, this hypothesis is not applicable

Area of 6 urban district = 21443300 m2

21443300 ÷ 3 = 7147766.67 m2

3573883.33 ÷ 232583 = 15.3 m2

21443300 ÷ 2 = 10721650 m2

5360825 ÷ 232583 = 23 m2

reflectivity.

2680412.5 ÷ 232583 = 11.5 m2

materials, therefore:

2680412.5 m Higher albedo materials

#### **7.3. Analyze the model of natural ventilator of the city**

The area of 6 urban district is 2144.33 hectare with population of 232583. According to Table 4, vegetation covers constitute 51.57 hectare of 6 urban district area. Therefore, per capita of vegetation cover in this district is 2.21m2.

Population of 6 urban district = 232583 Vegetation cover area = 51.57 hectare = 515700 m2 Therefore, per capita of vegetation cover = 2.21 m2

It means that only 2.4% of whole area of 6 urban district is composed of vegetation cover with 2.21 m2 per capita.

Based on studies and investigations of United Nation (UN) Environment, acceptable per capita of green spaces in cities is of between 20 and 25 m2 for each person [23].

In fact, 6 urban district with 2.21 m2 per capita of vegetation cover is around 18 m2 less than indicator of UN that makes the situation worse and increases UHI intensity in given area.

According to hypotheses, if a fourth area of 6 urban district is covered with vegetation covers and high albedo materials, therefore:

Area of 6 urban district = 21443300 m2

21443300 ÷ 4 = 5360825 m2

$$\begin{array}{rcl} \text{5360825} & \div \text{ 2 } = \text{ 2680412.5 m}^2 \longrightarrow \text{Vegetation covers} \\\\ \text{2680412.5 m}^2 \longrightarrow \text{Higher allbedo materials} \end{array}$$

Therefore, per capita of vegetation cover is:

2680412.5 ÷ 232583 = 11.5 m2

312 Air Pollution – A Comprehensive Perspective

Number 2 (Orange colour) shows the combination of higher albedo and non-vegetation with 739.16 hectare area, number 6 (yellow colour) shows the combination of medium albedo and non-vegetation with 1068.08 hectare area, number 9 (green colour) shows the combination of lower albedo and non-vegetation with 280.28 hectare area, which occupied 34.5%, 50% and 13% of 6 urban district respectively. Number 3 (red colour) shows the combination of higher albedo and vegetation with 48.80 hectare area, number 7 (brown colour) shows the combination of medium albedo and vegetation with 2.76 hectare area which occupied 2.27% and 0.12% of 6 urban district respectively. Other numbers are negligible which are not observed in Figure 35. Although number 3 includes high albedo and vegetation cover, it encompass the very low percentage of area which not only it has not impact on mitigation of UHI, but also this value of albedo (with value of 0.17-0.310) with lower reflectivity can increase the UHI intensity. The area of vegetation cover is negligible in comparison with whole area of the district. Number 6 with 50% of whole area of 6 urban district including non-vegetation cover and medium albedo materials (with value of 0.14-0.160) has been widely distributed in the district and provided worse condition for this district. After number 6, number 2 is in the worse condition with 34.5% of whole area of 6 urban district including non-vegetation cover and high albedo materials (with value of 0.17-0.310). Then number 9 with 13% of whole area of 6 urban district and the combination of lower albedo and non-vegetation cover stands in the next rank. It has been widely distributed in region 1 and 2 that the topography of these regions also provided higher UHI intensity. It is also

The area of 6 urban district is 2144.33 hectare with population of 232583. According to Table 4, vegetation covers constitute 51.57 hectare of 6 urban district area. Therefore, per capita of

It means that only 2.4% of whole area of 6 urban district is composed of vegetation cover

Based on studies and investigations of United Nation (UN) Environment, acceptable per

In fact, 6 urban district with 2.21 m2 per capita of vegetation cover is around 18 m2 less than indicator of UN that makes the situation worse and increases UHI intensity in given area.

According to hypotheses, if a fourth area of 6 urban district is covered with vegetation

capita of green spaces in cities is of between 20 and 25 m2 for each person [23].

observed in Figure 16 that these regions have higher UHI impacts.

**7.3. Analyze the model of natural ventilator of the city**

vegetation cover in this district is 2.21m2.

Vegetation cover area = 51.57 hectare = 515700 m2 Therefore, per capita of vegetation cover = 2.21 m2

covers and high albedo materials, therefore:

Area of 6 urban district = 21443300 m2

21443300 ÷ 4 = 5360825 m2

Population of 6 urban district = 232583

with 2.21 m2 per capita.

In comparison with the UN indicator (20-25 m2), it is 8.5 m2 less.

Therefore, this hypothesis is not applicable

If a third area of 6 urban district is covered with vegetation covers and high albedo materials, therefore:

Area of 6 urban district = 21443300 m2

21443300 ÷ 3 = 7147766.67 m2

2 2 7147766.67 2 3573883.33 m Vegetation covers 3573883.33m Higher albedo materials

Therefore, per capita of vegetation cover is:

3573883.33 ÷ 232583 = 15.3 m2

In comparison with the UN indicator (20-25 m2), it is still 5 m2 less.

Therefore, this hypothesis also is not applicable.

If the amount of vegetation cover and high albedo material increase and cover half of urban area, the effect of UHI can be achieved zero which is the ideal condition, therefore:

Area of 6 urban district = 21443300 m2

21443300 ÷ 2 = 10721650 m2

2 2 10721650 2 5360825m Vegetation covers 5360825 m Higher albedo materials

Therefore, per capita of vegetation cover is:

5360825 ÷ 232583 = 23 m2

In comparison with the UN indicator (20-25 m2), it is acceptable.

Therefore, this hypothesis is applicable.

In addition, as Reagan and Acklam [24] calculated, when the reflectivity of the rest of area (5360825 m2) with poorly insulated building is increased from 0.25 to 0.65, the heat gains through the roof are reduced by half. It means that the albedo values mentioned in Table 2 is higher albedo in current classification of 6 urban district and it does not have higher reflectivity.

Figure 36 shows the areas with higher intensity of UHI chosen to implement natural ventilator model as shown in Figure 18. These areas cover approximately half area of 6 urban district with vegetation cover along with high albedo material and they act as ventilation holes and mitigate UHI effects.

Mitigating Urban Heat Island Effects in Tehran Metropolitan Area 315

The boundary condition was set according to the current situation of model area based on weather data obtained from the mobile survey. One typical time scenario, 1200 hr, was

Figure 37 illustrates the current situation of the model area and three scenarios in ENVI-met. The material used for buildings, in current situation of 6 urban district, is concrete with albedo of 0.30, for roofs and roads is asphalt with albedo of 0.14-0.16 and some parts of the area have covered by loamy soil with albedo of 0.17-0.23. There is the lack of vegetation cover in this area. In scenario 1, low albedo materials were changed to high albedo one, asphalt to bright asphalt with albedo of 0.55, concrete was covered with white coating with albedo of 0.85, and loamy soil to light colored soil with albedo of 0.6. In scenario 2, horizontal and vertical surfaces were covered by vegetation cover. In fact, these two scenarios show that how vegetation and high albedo material can contribute to UHI mitigation separately. In scenario 3, the model area was covered with high albedo material along with vegetation cover in order to see that how these two variables can contribute

selected for analysis.

together to mitigate the effect of UHI.

(294.60 K) were observed.

**Figure 37.** Current situation of the model area and three scenarios in ENVI-met

Figure 38 shows the daytime (at 1200 hr) simulation of the current situation of 6 urban district and three scenarios. As seen in this Figure, in current situation of 6 urban district, higher temperature (above 295.80 K) occurs in roads and roof of buildings with low albedo material such as asphalt and the areas with less vegetation cover. The simulation results show that when the points are closer to the green area (east north), lower temperatures

**Figure 36.** Covering the half area of 6 urban district with vegetation cover along with high albedo material

#### **7.4. Simulation through ENVI-met**

ENVI-met was employed to simulate "natural ventilator of the city" (NVC) model. This simulation compares the current situation of 6 urban district of Tehran with three scenarios according to the variable of the NVC model. These three scenarios were created as follows:

The boundary condition was set according to the current situation of model area based on weather data obtained from the mobile survey. One typical time scenario, 1200 hr, was selected for analysis.

314 Air Pollution – A Comprehensive Perspective

material

**7.4. Simulation through ENVI-met**

ventilation holes and mitigate UHI effects.

Figure 36 shows the areas with higher intensity of UHI chosen to implement natural ventilator model as shown in Figure 18. These areas cover approximately half area of 6 urban district with vegetation cover along with high albedo material and they act as

**Figure 36.** Covering the half area of 6 urban district with vegetation cover along with high albedo

ENVI-met was employed to simulate "natural ventilator of the city" (NVC) model. This simulation compares the current situation of 6 urban district of Tehran with three scenarios according to the variable of the NVC model. These three scenarios were created as follows:

Scenario 3: cover the model area with vegetation cover along with high albedo material.

Scenario 1: change current low albedo material to high albedo materials;

Scenario 2: cover the model area with vegetation cover; and

Figure 37 illustrates the current situation of the model area and three scenarios in ENVI-met. The material used for buildings, in current situation of 6 urban district, is concrete with albedo of 0.30, for roofs and roads is asphalt with albedo of 0.14-0.16 and some parts of the area have covered by loamy soil with albedo of 0.17-0.23. There is the lack of vegetation cover in this area. In scenario 1, low albedo materials were changed to high albedo one, asphalt to bright asphalt with albedo of 0.55, concrete was covered with white coating with albedo of 0.85, and loamy soil to light colored soil with albedo of 0.6. In scenario 2, horizontal and vertical surfaces were covered by vegetation cover. In fact, these two scenarios show that how vegetation and high albedo material can contribute to UHI mitigation separately. In scenario 3, the model area was covered with high albedo material along with vegetation cover in order to see that how these two variables can contribute together to mitigate the effect of UHI.

**Figure 37.** Current situation of the model area and three scenarios in ENVI-met

Figure 38 shows the daytime (at 1200 hr) simulation of the current situation of 6 urban district and three scenarios. As seen in this Figure, in current situation of 6 urban district, higher temperature (above 295.80 K) occurs in roads and roof of buildings with low albedo material such as asphalt and the areas with less vegetation cover. The simulation results show that when the points are closer to the green area (east north), lower temperatures (294.60 K) were observed.

In scenario 1, the cooling effect of high albedo materials can be seen in the simulation (Figure 37). In the east north part, it is not seen higher temperature while in current situation there is higher temperature and it decreased to around between 294.20 and 295.20 K and in building area to around less than 294K. It means that high albedo materials have extreme effects on decreasing the ambient temperature.

Mitigating Urban Heat Island Effects in Tehran Metropolitan Area 317

In scenario 3, the combination of vegetation and high albedo material has been examined in order to test that how these two variables can affect the surrounding built-up area in parallel. As seen in Figure 38, in scenario 3, it is obvious that the combination of these two variables can affect to reduce the temperature around 2.43°C. Scenarios' 1 and 2 also contribute to reduce the temperature singly, while in scenario 3 which is the combination of

Therefore, in the cross-comparison of the three scenarios for temperature, the best cooling effect on the surrounding built-up area is observed in the third scenario and cooling effect of

[1] Emmanuel M.R 2005. An Urban Approach to Climate-Sensitive Design; Strategies for

[2] Asimakopoulos D.N, Assimakopoulos V.D, Chrisomallidou N, Klitsikas N, Mangold D, Michel P, Santamouris M, Tsangrassoulis A (2001) Energy and Climate in the Urban

[6] Oke T.R (2006) Initial Guidance to Obtain Representative Meteorological Observations

[7] Oke T.R (2006) Towards Better Scientific Communication in Urban Climate. Theoretical

[8] Bridgman H, Warner R, Dodson J (1995) Urban Biophysical Environments. Melbourne

[9] Grimmond S (2007) Urbanization and Global Environmental Change: Local Effects of

[10] Oke T.R (1981) Canyon Geometry and the Nocturnal Urban Heat Island: Comparison of

[11] Santamouris M (2001) Energy and Climate in the Urban Built Environment. London.

Built Environment. M. Santamouris (Ed.). London. James & James Publication. [3] Oke T.R (1987) Boundary Layer Climates (2an edn.). New York. Methuen and Co. Ltd. [4] Wong N.H, Chen Y (2009) Tropical Urban Heat Islands: Climate, Buildings and

[5] Glantz M.H (2005) What Makes Good Climates Go Bad?. Geotimes 50(4):18-24.

at Urban Sites.Instruments and Observing Methods. Canada. WHO. No. 81.

Urban Warming. Cities and Global Environmental Change 83-88.

Scale Model and Field Observations. Journal of Climatology 1: 237-254.

scenario 1 and 2 has extreme contribution to mitigate the air temperature.

Parisa Shahmohamadi, Ulrich Cubasch and Sahar Sodoudi *Institut für Meteorologie, Freie Universität Berlin, Germany*

*Universiti Kebangsaan Malaysia, Selangor, Malaysia*

the Tropics. London. Spon Press.

and Applied Climatology 84: 179-190.

James & James Publication.

and New York: Oxford University Press.

*Department of Architecture, Faculty of Engineering and Built Environment,*

Greenery. London and New York. Taylor & Francis Press.

**Author details**

A.I. Che-Ani

**8. References**

greenery along with high albedo material can be confirmed by the simulation.

**Figure 38.** Daytime simulation of 6 urban district and three scenarios in ENVI-met

When the vegetation is added in scenario 2, the temperature in the areas with trees and roof gardens has been reduced from around 295 K to 294.20 K. In fact, the moisture levels in the soil dose not cause the temperature to be similar to those on the hard pavement areas. Although the vegetation cover decreased temperature, there is still higher temperature in roads. Green roofs contributed to decreased the temperature in housing area. While the vegetation is replaced with hard pavement (current situation), it can be seen that the whole area now has a higher temperature at about above 295.80 K. The qualitative analysis of the temperature data showed that the coolest areas were in the Saee and Laleh parks located in route number 3. It means that field measurement has also shown the same results that greenery can decrease temperature. The reduction of the air temperature in the areas with more vegetation cover can reach 0.8 °C. In the comparison of the scenarios' 1 and 2 and current situation for temperature, scenario 1 has more effect on the surrounding built-up area than vegetation cover.

In scenario 3, the combination of vegetation and high albedo material has been examined in order to test that how these two variables can affect the surrounding built-up area in parallel. As seen in Figure 38, in scenario 3, it is obvious that the combination of these two variables can affect to reduce the temperature around 2.43°C. Scenarios' 1 and 2 also contribute to reduce the temperature singly, while in scenario 3 which is the combination of scenario 1 and 2 has extreme contribution to mitigate the air temperature.

Therefore, in the cross-comparison of the three scenarios for temperature, the best cooling effect on the surrounding built-up area is observed in the third scenario and cooling effect of greenery along with high albedo material can be confirmed by the simulation.

#### **Author details**

316 Air Pollution – A Comprehensive Perspective

Y (m)

Y (m)

area than vegetation cover.

effects on decreasing the ambient temperature.

X (m) <sup>0</sup> <sup>10</sup> <sup>20</sup> <sup>30</sup> <sup>40</sup> <sup>50</sup> <sup>60</sup> <sup>70</sup> <sup>80</sup> <sup>90</sup> <sup>100</sup> <sup>110</sup> <sup>120</sup> <sup>130</sup> <sup>140</sup> <sup>150</sup> <sup>160</sup> <sup>170</sup> <sup>180</sup>

0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180

In scenario 1, the cooling effect of high albedo materials can be seen in the simulation (Figure 37). In the east north part, it is not seen higher temperature while in current situation there is higher temperature and it decreased to around between 294.20 and 295.20 K and in building area to around less than 294K. It means that high albedo materials have extreme

**Pot.  Daytime simulation of 6 urban district and three scenarios in ENVI-met

Current situation of 6 urban district Scenario 1

Y (m)

Y (m)

When the vegetation is added in scenario 2, the temperature in the areas with trees and roof gardens has been reduced from around 295 K to 294.20 K. In fact, the moisture levels in the soil dose not cause the temperature to be similar to those on the hard pavement areas. Although the vegetation cover decreased temperature, there is still higher temperature in roads. Green roofs contributed to decreased the temperature in housing area. While the vegetation is replaced with hard pavement (current situation), it can be seen that the whole area now has a higher temperature at about above 295.80 K. The qualitative analysis of the temperature data showed that the coolest areas were in the Saee and Laleh parks located in route number 3. It means that field measurement has also shown the same results that greenery can decrease temperature. The reduction of the air temperature in the areas with more vegetation cover can reach 0.8 °C. In the comparison of the scenarios' 1 and 2 and current situation for temperature, scenario 1 has more effect on the surrounding built-up Parisa Shahmohamadi, Ulrich Cubasch and Sahar Sodoudi *Institut für Meteorologie, Freie Universität Berlin, Germany*

A.I. Che-Ani

*Department of Architecture, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Selangor, Malaysia*

#### **8. References**

	- [12] Roth M (2002) Effects of Cities on Local Climates, Proceedings of Workshop of IGES/APN Mega-City Project, 23-25 January 2002, Kitakyushu Japan.

**Chapter 12**

© 2012 Chen, licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,

distribution, and reproduction in any medium, provided the original work is properly cited.

© 2012 Chen, licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**Managing Emergency Response**

Additional information is available at the end of the chapter

Wang-Kun Chen

**1. Introduction**

http://dx.doi.org/10.5772/50080

**of Air Pollution by the Expert System**

Recently, the emergency preparedness of environmental disaster has been grown because of the climate change and growth of new technology in industry. The need to reduce the risk of major event of air pollution is of great concern. To ensure the quality of response management and reduce the loss in the air pollution event, it is necessary to design a reliable emergency response system. However, the phenomenon of air pollution is very

A well prepared response management plan should include the prediction and recommendation for the policy makers so as to reduce the possible damage of the disaster. This chapter sets out the method to improve both planning for emergency response of air

The effect of air pollution includes the long-term and short term. Long-term effect of air pollution was controlled by the abatement program of source reduction. However, the short-term episode is more difficult to control because the emergency response is usually

The environmental disaster, both from the natural and man-made release, has to be controlled by the well-designed management program. However, the consequence of the disaster was related to so many actions and regulations, therefore it is very difficult to make a quick and correct response measure only by the human. The supplementary system with

The decision making system for air pollution management has to consider the appropriate method to avoid the damage from the pollution. Therefore, a complete database includes all the possible reason and consequence results should be included in this system. Beside, the

the aid of computer system become more important in the decision making process.

complicated so it is very difficult to consider all possible factors in one system.

pollution and recommendations to improve the effectiveness of this system.

very complicated and related to many people in the neighboring area.

### **Managing Emergency Response of Air Pollution by the Expert System**

Wang-Kun Chen

318 Air Pollution – A Comprehensive Perspective

107:167–205.

[12] Roth M (2002) Effects of Cities on Local Climates, Proceedings of Workshop of

[13] Mayer H (1992) PlannungsfaktorStadtklima. Münchner Forum, Berichte und Protokolle,

[15] Akbari H, Davis S, Dosano S, Huang J,Winnett S (Editors) (1992) Cooling our Communities: a Guidebook on Tree Planting and Light-Colored Surfacing. United

[16] Halvorson H, Potts D (1981) Water Requirements of Honeylocust (Gleditsiatriacanthos f. inermis) in the Urban Forest. USDA Forest Service Research Paper. NE-487. [17] Heilman J, Brittin C, Zajicek J (1989) Water Use by Shrubs as Affected by Energy Exchange with Building Walls. Agricultural and Forest Meteorology 48: 345-357. [18] Akbari H, Pomerantz M, Taha H (2001) Cool Surfaces and ShadeTrees to Reduce Energy Use and Improve Air Quality in Urban Areas.Solar Energy 70(3): 295–310. [19] Simpson J.R, McPhersonE.G (1997) The Effect of Roof Albedo Modification on Cooling Loads of Scale Model Residences in Tucson, Arizona. Energy and Buildings 25: 127-137. [20] Goward S.N, Cruickshanks G.D, Hope A.S (1985) Observed Relation between Thermal Emission and Reflected Spectral Radiance of a Complex Vegetated Landscape. Remote

[21] Lougeay R, Brazel A, Hubble M (1996) Monitoring Intra-Urban Temperature Patterns and Associated Land Cover in Phoenix; Arizona Using Landsat Thermal Data.Geocarto

[22] Bretz S, Akbari H, Rosenfield A, Taha H (1992) Implementation of Solar Reflective Surface: Materials and Utility Programs. LBL Report 32467. University of California.

[24] Reagan J.A, Acklam D.M (1979) Solar Reflectivity of Common Building Materials and its Influence on the Roof Heat Gain of Typical Southwestern USA Residences. Energy

IGES/APN Mega-City Project, 23-25 January 2002, Kitakyushu Japan.

[14] Fezer F(1995) Das KlimaderStädt. Gotha, Justus PerthesVerlag.

States Environmental Protection Agency, Washington, D.C.

Sensing of Environment 18: 137-146.

[23] United Nation Environment (2010)http://www.unep.org/.

International 11: 79-89.

and Buildings 2: 237-248.

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/50080

### **1. Introduction**

Recently, the emergency preparedness of environmental disaster has been grown because of the climate change and growth of new technology in industry. The need to reduce the risk of major event of air pollution is of great concern. To ensure the quality of response management and reduce the loss in the air pollution event, it is necessary to design a reliable emergency response system. However, the phenomenon of air pollution is very complicated so it is very difficult to consider all possible factors in one system.

A well prepared response management plan should include the prediction and recommendation for the policy makers so as to reduce the possible damage of the disaster. This chapter sets out the method to improve both planning for emergency response of air pollution and recommendations to improve the effectiveness of this system.

The effect of air pollution includes the long-term and short term. Long-term effect of air pollution was controlled by the abatement program of source reduction. However, the short-term episode is more difficult to control because the emergency response is usually very complicated and related to many people in the neighboring area.

The environmental disaster, both from the natural and man-made release, has to be controlled by the well-designed management program. However, the consequence of the disaster was related to so many actions and regulations, therefore it is very difficult to make a quick and correct response measure only by the human. The supplementary system with the aid of computer system become more important in the decision making process.

The decision making system for air pollution management has to consider the appropriate method to avoid the damage from the pollution. Therefore, a complete database includes all the possible reason and consequence results should be included in this system. Beside, the

© 2012 Chen, licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2012 Chen, licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

system should be able to deduce the possible consequence and suggest the best choice for the decision makers.

Managing Emergency Response of Air Pollution by the Expert System 321

The design of this system includes the following steps as: (1) Identify problem characteristics; (2) Find concept to represent knowledge; (3) Design structure to organize knowledge; (4)

Every air pollution event has its characteristics; it can be represents by an appropriate knowledge. Then we design the structure to organize this knowledge, and formulate the mathematical rules to embody the knowledge so that it can be inference in the system. Finally, we have to validate the rules and organize all of this knowledge for the future

The air pollution disaster is an accidental phenomenon in the environment; it can be represented by a mathematical pattern. The pattern structure of air pollution management can be categorized as the following four types as shown in figure 3. The first pattern is the characteristic of the air pollution episode itself. The second pattern is the change pattern in the ecosystem. The third pattern is the loss pattern in the economic system. And the final

Different episode has different characteristics, such as the dust storm, forest fire, explosion, and toxic gas leakage, etc. it will cause various types of damages. These will cause the damage in ecosystems and loss in economic system. Therefore, the change in these two systems has to be the well prepared management systems. A good management system should be able to concern all of these factors together. And suggest an optimum decision for the decision maker. However, it will include many criteria in the thinking, so the expert

Formulate rules to embody knowledge; (5) Validate rules that organize knowledge.

forecasting.

**Figure 2.** Flow chart for constructing an expert system

system has to be applied in the solution.

**2.2. Identify the pattern system of risk management**

pattern is the response pattern for the disaster management.

A case study was presents in this chapter. This study uses the experience in Taiwan as an example. Since Taiwan is a very small island with highest population density, the air pollution also causes severe problem for the public. Because the population density is as high as the on the top of the world, the air pollution response management system have also received more attention in environmental management.

The chapter was written in the following structure. The concept of air pollution management was described in the second section. Then the structure of knowledge bank was proposed. The data base and inference system was proposed and written in the following. Finally a conclusion of this system and the suggestion for the future research was followed.

#### **2. Concept of air pollution management system**

#### **2.1. Definition of air pollution disaster and risk management pattern**

Before going into the detail, we have to know the concept of air pollution management. An air pollution management system for emergency response could be described by figure 1. In this system, there includes a knowledge database, an inference mechanism, and the interface with the users and another resource. Because the system will influence many people and interest groups, so it has to be designed more carefully in order to get the optimum decision. This data base and inference mechanism is just used to ensure the reliability of the effectiveness of this system.

**Figure 1.** Expert system for air pollution emergency response

The design of this system includes the following steps as: (1) Identify problem characteristics; (2) Find concept to represent knowledge; (3) Design structure to organize knowledge; (4) Formulate rules to embody knowledge; (5) Validate rules that organize knowledge.

Every air pollution event has its characteristics; it can be represents by an appropriate knowledge. Then we design the structure to organize this knowledge, and formulate the mathematical rules to embody the knowledge so that it can be inference in the system. Finally, we have to validate the rules and organize all of this knowledge for the future forecasting.

**Figure 2.** Flow chart for constructing an expert system

320 Air Pollution – A Comprehensive Perspective

received more attention in environmental management.

**2. Concept of air pollution management system**

**Figure 1.** Expert system for air pollution emergency response

**2.1. Definition of air pollution disaster and risk management pattern**

the decision makers.

followed.

effectiveness of this system.

system should be able to deduce the possible consequence and suggest the best choice for

A case study was presents in this chapter. This study uses the experience in Taiwan as an example. Since Taiwan is a very small island with highest population density, the air pollution also causes severe problem for the public. Because the population density is as high as the on the top of the world, the air pollution response management system have also

The chapter was written in the following structure. The concept of air pollution management was described in the second section. Then the structure of knowledge bank was proposed. The data base and inference system was proposed and written in the following. Finally a conclusion of this system and the suggestion for the future research was

Before going into the detail, we have to know the concept of air pollution management. An air pollution management system for emergency response could be described by figure 1. In this system, there includes a knowledge database, an inference mechanism, and the interface with the users and another resource. Because the system will influence many people and interest groups, so it has to be designed more carefully in order to get the optimum decision. This data base and inference mechanism is just used to ensure the reliability of the

#### **2.2. Identify the pattern system of risk management**

The air pollution disaster is an accidental phenomenon in the environment; it can be represented by a mathematical pattern. The pattern structure of air pollution management can be categorized as the following four types as shown in figure 3. The first pattern is the characteristic of the air pollution episode itself. The second pattern is the change pattern in the ecosystem. The third pattern is the loss pattern in the economic system. And the final pattern is the response pattern for the disaster management.

Different episode has different characteristics, such as the dust storm, forest fire, explosion, and toxic gas leakage, etc. it will cause various types of damages. These will cause the damage in ecosystems and loss in economic system. Therefore, the change in these two systems has to be the well prepared management systems. A good management system should be able to concern all of these factors together. And suggest an optimum decision for the decision maker. However, it will include many criteria in the thinking, so the expert system has to be applied in the solution.

**Figure 3.** Pattern structures of environmental risk and its management

#### **2.3. Define the space-time information system of air pollution risk management**

In designing the expert system, the risk of air pollution event should be discussed first. Since the air pollutant or hazardous materials can be released into the atmosphere by accidents at plants, chemical processing, and other facilities. They may also release by transportation accidents. All of these events can cause the risk for the residents in the neighboring communities. Thus a precise way to estimate the possible damage to the community of the environment is indeed very important. The formulation of a disaster event has to be derived first.

The process of air pollution disaster management is a space-time information problem. In general, the space geographical information can be represented by the following equation:

$$I = \sum\_{i=1}^{m} \left[ \sum\_{j=1}^{n} \begin{bmatrix} S\_{ij} \begin{pmatrix} T\_{ij} \\ \vdots \end{pmatrix} \\ A\_{ij} \begin{pmatrix} T\_{ij} \\ \end{pmatrix} \\ T\_{ij} \end{pmatrix} \right] \tag{1}$$
  $T\_{ij} \{ t\_{b'}, t\_{e} \}\_{ij}$

Managing Emergency Response of Air Pollution by the Expert System 323

*n*

(2)

From the above equation, all the events can be described and all the influence of this event in different space can be explained. The remaining parts are to transform the actual events

For an air pollution disaster management system, there exists a domain of emergency

*n*

Where M is the collection of emergency response measure; Action i is the individual vector for the ith measure. Each action is represented by a symbol m; and there are m measures in the action domain. If the risk management system is good enough, there should have enough measure to solve the problem encountered by the air pollution. Therefore, we have

[Inference 3] If i is constant, then for each Sij(Tij) and Aij(Tij), there exists a measure mi in the

[Inference 4] If j is constant, then for each Sij(Tij) and Aij(Tij), there exists a measure mi in the

Different actions have different effectiveness. For example, the authority can stop the emission of air pollutants from the plant in case of necessary. Or they may restrict the activity of community people when there is a need to reduce the emission from the sources. Most of the action related to the benefit of the community people, so different divisions of the government has to be involved, like the local government, environmental protection

Not every action has a significant effect on the reduction of disaster, and they costs different budget. Thus we have to be very careful in selecting the actions. All the assembly of these

The knowledge bank of an expert system is shown as figure 4. There are different types of knowledge storaged in this system, like the events pattern, the change pattern of economic

actions is the policy of the government. It is recommended by the expert system.

, ,,

*Action Action Action*

**2.4. Define the emergency response measure for risk management**

 1 2

,, ,

*mm m*

1 2

*m*

1

agency, chamber of commerce, and regional development agency etc.

**3.1. Construct the modeling base for the expert system**

system, the change pattern of ecological system, and others.

*n i i*

*M*

into the mathematical forms for further inference.

response measures, defined by the following equation:

the following inferences:

emergency response measures domain.

emergency response measures domain.

**3. Knowledge-bank analysis**

Where I is the collection of space geographical information; it is the individual vector for the ith item, j is the state of this item; Sij(Tij) and Aij(Tij) represent the characteristics of this item in time tb to te. The inferences of the above equation are the following:

[Inference 1 ]If i is constant, then this equation represents the time series data of the same characteristic.

[Inference 2] If j is constant, then this equation represents the characteristic distribution in the same time.

From the above equation, all the events can be described and all the influence of this event in different space can be explained. The remaining parts are to transform the actual events into the mathematical forms for further inference.

#### **2.4. Define the emergency response measure for risk management**

322 Air Pollution – A Comprehensive Perspective

first.

characteristic.

the same time.

**Figure 3.** Pattern structures of environmental risk and its management

**2.3. Define the space-time information system of air pollution risk management**

In designing the expert system, the risk of air pollution event should be discussed first. Since the air pollutant or hazardous materials can be released into the atmosphere by accidents at plants, chemical processing, and other facilities. They may also release by transportation accidents. All of these events can cause the risk for the residents in the neighboring communities. Thus a precise way to estimate the possible damage to the community of the environment is indeed very important. The formulation of a disaster event has to be derived

The process of air pollution disaster management is a space-time information problem. In general, the space geographical information can be represented by the following equation:

*ij ij m n*

Where I is the collection of space geographical information; it is the individual vector for the ith item, j is the state of this item; Sij(Tij) and Aij(Tij) represent the characteristics of this item in

[Inference 1 ]If i is constant, then this equation represents the time series data of the same

[Inference 2] If j is constant, then this equation represents the characteristic distribution in

1 1

*I AT*

*i j*

(,)

*Ttt*

time tb to te. The inferences of the above equation are the following:

*ij b e ij*

*S T*

*ij ij*

(1)

*ij*

*T*

For an air pollution disaster management system, there exists a domain of emergency response measures, defined by the following equation:

$$\begin{aligned} &= \left(Action\_1, Action\_2, \dots, Action\_n \right) \\ &= \left( m\_1, m\_2, \dots, \dots, m\_n \right) \\ &= \sum\_{i=1}^n m\_i \end{aligned} \tag{2}$$

Where M is the collection of emergency response measure; Action i is the individual vector for the ith measure. Each action is represented by a symbol m; and there are m measures in the action domain. If the risk management system is good enough, there should have enough measure to solve the problem encountered by the air pollution. Therefore, we have the following inferences:

[Inference 3] If i is constant, then for each Sij(Tij) and Aij(Tij), there exists a measure mi in the emergency response measures domain.

[Inference 4] If j is constant, then for each Sij(Tij) and Aij(Tij), there exists a measure mi in the emergency response measures domain.

Different actions have different effectiveness. For example, the authority can stop the emission of air pollutants from the plant in case of necessary. Or they may restrict the activity of community people when there is a need to reduce the emission from the sources. Most of the action related to the benefit of the community people, so different divisions of the government has to be involved, like the local government, environmental protection agency, chamber of commerce, and regional development agency etc.

Not every action has a significant effect on the reduction of disaster, and they costs different budget. Thus we have to be very careful in selecting the actions. All the assembly of these actions is the policy of the government. It is recommended by the expert system.

#### **3. Knowledge-bank analysis**

#### **3.1. Construct the modeling base for the expert system**

The knowledge bank of an expert system is shown as figure 4. There are different types of knowledge storaged in this system, like the events pattern, the change pattern of economic system, the change pattern of ecological system, and others.

Managing Emergency Response of Air Pollution by the Expert System 325

banks should be contained in the system, which are meteorological model, air quality

The wind field model helps us to know the possible damage of the episode. Temperature variation model provide us the diffusion capacity of the atmosphere. The pollutant path prediction model helps us to identify the duty of the polluter. A terrain model provides us the information for the safety management of this event. The cloud model is benefit for the estimation of precipitation of pollutant. And the vertical wind distribution model provides

There are several approaches to calculate air pollution diffusion. The most famous are the

The above three model has different capabilities. Gaussian diffusion model is suitable for the near field forecasting. Trajectory model are often used to know the source-receptor relationship and suggest the possible decision for pollution abatement. The grid model can treat the photochemical reaction and often used in the implementation management

Recently, the improvements in computer technology have significantly improved the speed and accuracy of air quality models. These models have been found in many different areas from ensuring regulatory compliance to assessing human exposure to natural disaster,

model, and economic model. **1. Meteorological model:**

2. Temperature variation model 3. Pollutant path prediction model

6. Vertical wind distribution models

1. Wind field model

4. Terrain model 5. Cloud model

**2. Air quality model:**

following three types.

2. Trajectory model 3. Grid model

program of air pollution.

**3. Economic models:**

1. Housing damage model

3. Agricultural loss models 4. Indirect economic loss models

2. Personal injury and death model

1. Gaussian diffusion model

The meteorological model includes the following:

7. Remote sensing generated meteorological parameter model

us the understanding of vertical diffusion capability of the atmosphere.

accidental release, and intentional air pollutant transport.

The economic models include the following:

**Figure 4.** Intelligent knowledge-based expert system

The entire possible pattern, such as the pollution prediction, hazard identification, pollution distribution, forest ecosystem, land economic, and risk management are included in the knowledge database. With this information, the system is able to forecast the possible outcome of the pollution disaster so that the residents can determine the best prevention strategy. Other tools like the logic operation, decision table, and fault tree analysis technique should be included. Finally, a developer interface and the user interface have to be designed very carefully.

The knowledge bank contains the model bank, pattern bank, and regulation bank as below. [5] There are three main modeling activities which included in the expert system: (1) contingency modeling, (2) short term modeling, and (3) accidental, or release modeling [6].

Contingency modeling is to present concentrations for specific chemicals and emission, which may be encountered at a possible release place. Short term modeling is to calculate concentrations occurred in a short periods. The third modeling, accidental release modeling, is perhaps the most critical to emergency managers, which includes natural or accidental release. This type of release modeling is performed soon after a release occurs and is proposed to give immediate responses.

#### **3.2. The model bank for expert system**

In designing the expert system for air pollution management, we have to analysis the necessary model as the tool for choosing the correct response measures. Three major model banks should be contained in the system, which are meteorological model, air quality model, and economic model.

#### **1. Meteorological model:**

The meteorological model includes the following:

1. Wind field model

324 Air Pollution – A Comprehensive Perspective

**Figure 4.** Intelligent knowledge-based expert system

proposed to give immediate responses.

**3.2. The model bank for expert system**

very carefully.

The entire possible pattern, such as the pollution prediction, hazard identification, pollution distribution, forest ecosystem, land economic, and risk management are included in the knowledge database. With this information, the system is able to forecast the possible outcome of the pollution disaster so that the residents can determine the best prevention strategy. Other tools like the logic operation, decision table, and fault tree analysis technique should be included. Finally, a developer interface and the user interface have to be designed

The knowledge bank contains the model bank, pattern bank, and regulation bank as below. [5] There are three main modeling activities which included in the expert system: (1) contingency modeling, (2) short term modeling, and (3) accidental, or release modeling [6]. Contingency modeling is to present concentrations for specific chemicals and emission, which may be encountered at a possible release place. Short term modeling is to calculate concentrations occurred in a short periods. The third modeling, accidental release modeling, is perhaps the most critical to emergency managers, which includes natural or accidental release. This type of release modeling is performed soon after a release occurs and is

In designing the expert system for air pollution management, we have to analysis the necessary model as the tool for choosing the correct response measures. Three major model

The wind field model helps us to know the possible damage of the episode. Temperature variation model provide us the diffusion capacity of the atmosphere. The pollutant path prediction model helps us to identify the duty of the polluter. A terrain model provides us the information for the safety management of this event. The cloud model is benefit for the estimation of precipitation of pollutant. And the vertical wind distribution model provides us the understanding of vertical diffusion capability of the atmosphere.

#### **2. Air quality model:**

There are several approaches to calculate air pollution diffusion. The most famous are the following three types.

The above three model has different capabilities. Gaussian diffusion model is suitable for the near field forecasting. Trajectory model are often used to know the source-receptor relationship and suggest the possible decision for pollution abatement. The grid model can treat the photochemical reaction and often used in the implementation management program of air pollution.

Recently, the improvements in computer technology have significantly improved the speed and accuracy of air quality models. These models have been found in many different areas from ensuring regulatory compliance to assessing human exposure to natural disaster, accidental release, and intentional air pollutant transport.

#### **3. Economic models:**

The economic models include the following:

5. Post-disaster reconstruction costs model

#### **3.3. The pattern bank for expert system**

Three mathematical methods could be applied in the treatment of pattern in this research, which are: (1) Statistical pattern ;( 2) Fuzzy pattern ;( 3) Neural-network pattern. [2] [3] [4] [7]

Managing Emergency Response of Air Pollution by the Expert System 327

types of spatial data(i.e. from isoclines symbols on a topographic map to point

2. A data storage and retrial subsystem that organizes the spatial data in a manner that

3. A data manipulation and analysis subsystem that performs tasks on the data, aggregates and disaggregates, estimates parameters and constraints, and performs

4. A reporting subsystem that display all or part of the database in tabular, graphic, or

Logical formula operators allow us to compare values and evaluate the results. When two values are compared using logical operators, the result is either true or false. Logical operators are available in the Compute Wizard if/then/else formula menu as the following:

> The four quadrants Conditions Condition alternatives Actions Action entries

The inference mechanism in the decision supporting system includes the decision table, logic gate, decision tree, and fault tree etc. Decision tree analysis will be applied in the system for decision support. A decision tree is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event

In this study, we use the IF/THEN in the emergency response system. Table 2 is the example

In this study, we use the dust storm as an example for the emergency response system. A framework for the expert system was described in this section. This system provides a easyto-use, real-time access to pollution concentration predictions and consequence analysis. The system enables us to rapidly determine hazard areas, affected population, meteorological

of decision for an episode by the IF (information) / THEN (action) operator.

Different kinds of pattern model for air pollution management are listed in table 3.

**4.3. Models for air pollution emergency management**

elevations inside the GIS) [1]

**Table 1.** Example for conditions and actions

outcomes, resource costs, and utility.

**5. Case study results and discussion**

**5.1. Expert system for air pollution management**

conditions, and relevant geographical information

modeling functions.

map form.

allows retrieval, updating, and editing.

**4.2. Inference mechanism for the expert system**

In the system, we define the following systems: (1) Wind pattern; (2) Weather pattern; (3) Source pattern ;( 4) Population pattern.

#### **3.4. The regulation bank for expert system**

The risk management should follow the present regulations; therefore, a regulation bank for response measure is necessary in the management system.

#### **3.5. The action bank for expert system**

The action includes different economic models as the following.

#### **4. Data base analysis and inference system**

#### **4.1. Geographical information systems**

The tool capable to handle the figure and characters simultaneously is necessary for the research of air pollution emergency response system. The concept of geographical information system (GIS) could be the answer. GIS are tools that allow for the handing of spatial data into information. A lot OF GIS has been developed and applied in diverse field. For example, the GPS satellite system, the web-digital map, and the remote sensing technique, etc. They are all built with GIS as the core technology.

Geographical information system has several advantages over the traditional database. The major advantages include different treatment of characters, the ability to treat the map data, and desirable property to pose the data on internet. The tool contains report generating a summary to analyze the area affected by the air shed.

The GIS has the following subsystems:

1. A data input subsystem that collects and preprocesses spatial data from various sources. This subsystem is also largely responsible for the transformation of different types of spatial data(i.e. from isoclines symbols on a topographic map to point elevations inside the GIS) [1]

#### **4.2. Inference mechanism for the expert system**

Logical formula operators allow us to compare values and evaluate the results. When two values are compared using logical operators, the result is either true or false. Logical operators are available in the Compute Wizard if/then/else formula menu as the following:

**Table 1.** Example for conditions and actions

326 Air Pollution – A Comprehensive Perspective

5. Post-disaster reconstruction costs model

**3.3. The pattern bank for expert system**

Source pattern ;( 4) Population pattern.

**3.4. The regulation bank for expert system**

**3.5. The action bank for expert system**

3. Major disaster securities market model

**4.1. Geographical information systems**

**4. Data base analysis and inference system**

summary to analyze the area affected by the air shed.

The GIS has the following subsystems:

technique, etc. They are all built with GIS as the core technology.

1. Reinsurance compensation model

4. Social public disclosure models 5. Education and training model 6. Emergency response models 7. Human resource models

2. Super fund models

response measure is necessary in the management system.

The action includes different economic models as the following.

Three mathematical methods could be applied in the treatment of pattern in this research, which are: (1) Statistical pattern ;( 2) Fuzzy pattern ;( 3) Neural-network pattern. [2] [3] [4] [7]

In the system, we define the following systems: (1) Wind pattern; (2) Weather pattern; (3)

The risk management should follow the present regulations; therefore, a regulation bank for

The tool capable to handle the figure and characters simultaneously is necessary for the research of air pollution emergency response system. The concept of geographical information system (GIS) could be the answer. GIS are tools that allow for the handing of spatial data into information. A lot OF GIS has been developed and applied in diverse field. For example, the GPS satellite system, the web-digital map, and the remote sensing

Geographical information system has several advantages over the traditional database. The major advantages include different treatment of characters, the ability to treat the map data, and desirable property to pose the data on internet. The tool contains report generating a

1. A data input subsystem that collects and preprocesses spatial data from various sources. This subsystem is also largely responsible for the transformation of different The inference mechanism in the decision supporting system includes the decision table, logic gate, decision tree, and fault tree etc. Decision tree analysis will be applied in the system for decision support. A decision tree is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility.

In this study, we use the IF/THEN in the emergency response system. Table 2 is the example of decision for an episode by the IF (information) / THEN (action) operator.

#### **4.3. Models for air pollution emergency management**

Different kinds of pattern model for air pollution management are listed in table 3.

#### **5. Case study results and discussion**

#### **5.1. Expert system for air pollution management**

In this study, we use the dust storm as an example for the emergency response system. A framework for the expert system was described in this section. This system provides a easyto-use, real-time access to pollution concentration predictions and consequence analysis. The system enables us to rapidly determine hazard areas, affected population, meteorological conditions, and relevant geographical information

Managing Emergency Response of Air Pollution by the Expert System 329

Dust storm is a meteorological disaster which comes from the Mongolia area of northern China. The main reason for the formation of dust storm is the overdeveloping and the global warming which induce the soil become desert. The strong wind blow also increase the number of dust storm event year by year. This phenomenon also affects the neighboring

In order to realize the dust storm disaster, many researches has been made recently and the database was build. Most of the information about the dust storm was monitored by the meteorological and environmental monitoring system followed by the data processing

The information generated in the process was largely in the form of figure or character. Although it is convenient to understand, the time consuming in processing these information is too long. The new architecture in this study is capable to offer a function that enables us to search the map data directory from the dust storm event. The main advantage for the new architecture is to simplify the search work and save the time for searching dust

The case study described here referring to the design and algorithm of a dust storm event response system. The system was combined with the geographical information system and was called "DSGIS (Dust Storm Geographical Information System". DSGIS is an interactive geographical information system for dust storm research and has been developed to enhance the understanding of dust storm phenomenon and to offer a more convenient

The concept of geographical information system and supporting database system was applied in DSGIS for planning of the figureizational operating system of dust storm event. It enables use of digitization information to search and treat the dust storm event information. There are three major concerns in implementing a dust storm geographical information

Visual Basic was chosen to be the programming language of this system. The database applied in the preliminary system is "ACCESS" developed by Microsoft. GIS Design tool"ArcGIS ENGINE" supplied by ESRI was used in this system. And there are three

The dust storm events were gathered in the DSGIS database system and combined with the

In planning the database structure, the monitored data were collected first. A standard form was suggested to be the format of this database. There are five index of air pollutants in each monitoring station, they are total suspended particulate (TSP), sulfur dioxide (SO2), nitrogen

system as the following: programming language, database, GIS tool, and interface.

interfaces in the system: (1) User Interface (2) Developer Interface (3) Outer Interface.

**5.3. Design results of the expert system for managing air pollution**

**5.2. Design and capability of the emergency management systems**

area such as Korea, Japan, and Taiwan etc. [6]

environment for the researchers and public.

air quality monitoring station data.

procedure.

storm event.

**Table 2.** IF (information)/THEN (action) operator

**Table 3.** Four types of pattern in the emergency response system

Dust storm is a meteorological disaster which comes from the Mongolia area of northern China. The main reason for the formation of dust storm is the overdeveloping and the global warming which induce the soil become desert. The strong wind blow also increase the number of dust storm event year by year. This phenomenon also affects the neighboring area such as Korea, Japan, and Taiwan etc. [6]

328 Air Pollution – A Comprehensive Perspective

**Table 2.** IF (information)/THEN (action) operator

1 Wind field model Gaussian

2 Temperature variation

prediction model

distribution model

Air model pattern

Trajectory model

4 Cloud model Hybrid model Roads and bridges

6 Terrain model Post-disaster

**Table 3.** Four types of pattern in the emergency response system

diffusion model

Economic Pattern Risk management

Housing damage

Personal injury and death model

damage model

loss model

model

Indirect economic

reconstruction costs

model

Grid model Agricultural Loss Model

Pattern

model

model

Reinsurance

compensation model

Super fund model

Major disaster securities market

Social public disclosure model

Education and training model

Emergency Response

 Disaster Pattern

model

3 Pollutant path

5 Vertical wind

7 Remote sensing generated meteorological parameter model - In order to realize the dust storm disaster, many researches has been made recently and the database was build. Most of the information about the dust storm was monitored by the meteorological and environmental monitoring system followed by the data processing procedure.

The information generated in the process was largely in the form of figure or character. Although it is convenient to understand, the time consuming in processing these information is too long. The new architecture in this study is capable to offer a function that enables us to search the map data directory from the dust storm event. The main advantage for the new architecture is to simplify the search work and save the time for searching dust storm event.

#### **5.2. Design and capability of the emergency management systems**

The case study described here referring to the design and algorithm of a dust storm event response system. The system was combined with the geographical information system and was called "DSGIS (Dust Storm Geographical Information System". DSGIS is an interactive geographical information system for dust storm research and has been developed to enhance the understanding of dust storm phenomenon and to offer a more convenient environment for the researchers and public.

The concept of geographical information system and supporting database system was applied in DSGIS for planning of the figureizational operating system of dust storm event. It enables use of digitization information to search and treat the dust storm event information.

There are three major concerns in implementing a dust storm geographical information system as the following: programming language, database, GIS tool, and interface.

Visual Basic was chosen to be the programming language of this system. The database applied in the preliminary system is "ACCESS" developed by Microsoft. GIS Design tool"ArcGIS ENGINE" supplied by ESRI was used in this system. And there are three interfaces in the system: (1) User Interface (2) Developer Interface (3) Outer Interface.

#### **5.3. Design results of the expert system for managing air pollution**

The dust storm events were gathered in the DSGIS database system and combined with the air quality monitoring station data.

In planning the database structure, the monitored data were collected first. A standard form was suggested to be the format of this database. There are five index of air pollutants in each monitoring station, they are total suspended particulate (TSP), sulfur dioxide (SO2), nitrogen oxide (NOx), carbon monoxide (CO), and ozone (O3). The database was designed based on the above monitored information. However, the content of this system will be revised and expanded in case there is any change of demand for this system.

Managing Emergency Response of Air Pollution by the Expert System 331

**Figure 6.** Operation environment

**Entering into the system**

**Input the search condition**

of the four numbers.

area.

following.

The order of the system structure can be explained as in figure 6. The selected place or dust storm event can be input from the interface of the screen. Also, a "map searching" method was developed in this study. This method enables us to search a station directly from and display the information the users want to know. The detail of each step is described as the

The DSGIS will download the data automatically from the database of the selected zone

i. Latitude: input two sets of number of longitude and latitude such as (123, 23) and (121, 25). The number sets represent all the geographical information within the four points

ii. Date: input year, month, date, these data can be input simultaneously or separately, e.g.

iii. Location of the station: Input the station's number or the name of the station. It is also permissible to input two longitude and latitude to include all the stations within this

where the user entering into the interface of the operating environment.

Four search conditions can be used as the search condition, they are:

the data 20070409 represents all the data in April, 2007.

The dust storm determined the air quality, mainly in particulate. However, the concentration change during the dust storm period was also increased by the researchers. The air quality index PSI is automatically calculated by the above five air pollution concentration value and categorized as suggested by EPA. The core system and algorithm for DSGIS is described in the system. Following the database structure discussed in previous section, the infrasture of DSGIS is shown in figure 5.

**Figure 5.** The Dust Storm GIS system (DSGIS)

Managing Emergency Response of Air Pollution by the Expert System 331

**Figure 6.** Operation environment

330 Air Pollution – A Comprehensive Perspective

oxide (NOx), carbon monoxide (CO), and ozone (O3). The database was designed based on the above monitored information. However, the content of this system will be revised and

The dust storm determined the air quality, mainly in particulate. However, the concentration change during the dust storm period was also increased by the researchers. The air quality index PSI is automatically calculated by the above five air pollution concentration value and categorized as suggested by EPA. The core system and algorithm for DSGIS is described in the system. Following the database structure discussed in previous

expanded in case there is any change of demand for this system.

section, the infrasture of DSGIS is shown in figure 5.

**Figure 5.** The Dust Storm GIS system (DSGIS)

The order of the system structure can be explained as in figure 6. The selected place or dust storm event can be input from the interface of the screen. Also, a "map searching" method was developed in this study. This method enables us to search a station directly from and display the information the users want to know. The detail of each step is described as the following.

#### **Entering into the system**

The DSGIS will download the data automatically from the database of the selected zone where the user entering into the interface of the operating environment.

#### **Input the search condition**

Four search conditions can be used as the search condition, they are:

	- iv. Select the number of PSI as it is defined and all the information within this range can be retrieved.

Managing Emergency Response of Air Pollution by the Expert System 333

**5.5. Atmospheric transport and diffusion models of the expert system**

Gaussian diffusion model 1. ISCST

Trajectory model 1. CALPUF

Grid model 1. TAQM

**5.6. System validation and supporting databases of the expert system**

Air model pattern Name of the models

The supporting database is important because the changes in metrological conditions and in

A supporting database for the monitoring of the air quality data is important as explained in figure 8. the source distribution example of a county is shown in figure 9. the calculated pollution concentration in northern is shown in figure 10. Finally, the estimated social cost cause by air pollution in each district was shown in figure 11 as a for the decision maker.

**Figure 8.** The use of air quality monitoring data in the emergency response management system

2. AERMOD

2. GTx

2. CAMx 3. WRF

Some models were suggested in this system, as listed in Table 4.

**Table 4.** Atmospheric transport model used in the system

emission strengths may affect the air quality.

#### **Display the search results (Fig. 6)**

The information consistent with the search condition will be displayed The information consistent with the search condition will be displayed in the screen on its location with the following sign as◎, ●, ★, ☆, ▲, Δ, ♁, etc. .

#### **View the search results (Fig.7)**

When the search results were displayed, the DSGIS also allows the users the select the station directly through the mouse acted on the screen. More information about this station will be displayed consequently.

The version one of DSGIS has already completed. The structure of each component remains very flexible for the future application and adjustment of this system.

**Figure 7.** Search results

#### **5.4. Numerical weather prediction bank of the expert system**

In order to have precise results in the expert system, the numerical weather prediction model has to be applied. The prognostic data from numerical weather prediction models is suggested. The weather models predict future three dimensional atmosphere states by solving the conservation equations for mass, momentum, and thermodynamic energy. These models represent the relevant physical processes for moisture, cumulus convection, and radiation, and sub grid-scale turbulence.

#### **5.5. Atmospheric transport and diffusion models of the expert system**

Some models were suggested in this system, as listed in Table 4.

332 Air Pollution – A Comprehensive Perspective

**Display the search results (Fig. 6)**

**View the search results (Fig.7)**

will be displayed consequently.

**Figure 7.** Search results

following sign as◎, ●, ★, ☆, ▲, Δ, ♁, etc. .

retrieved.

iv. Select the number of PSI as it is defined and all the information within this range can be

The information consistent with the search condition will be displayed The information consistent with the search condition will be displayed in the screen on its location with the

When the search results were displayed, the DSGIS also allows the users the select the station directly through the mouse acted on the screen. More information about this station

The version one of DSGIS has already completed. The structure of each component remains

very flexible for the future application and adjustment of this system.

**5.4. Numerical weather prediction bank of the expert system**

and radiation, and sub grid-scale turbulence.

In order to have precise results in the expert system, the numerical weather prediction model has to be applied. The prognostic data from numerical weather prediction models is suggested. The weather models predict future three dimensional atmosphere states by solving the conservation equations for mass, momentum, and thermodynamic energy. These models represent the relevant physical processes for moisture, cumulus convection, **Table 4.** Atmospheric transport model used in the system

#### **5.6. System validation and supporting databases of the expert system**

The supporting database is important because the changes in metrological conditions and in emission strengths may affect the air quality.

A supporting database for the monitoring of the air quality data is important as explained in figure 8. the source distribution example of a county is shown in figure 9. the calculated pollution concentration in northern is shown in figure 10. Finally, the estimated social cost cause by air pollution in each district was shown in figure 11 as a for the decision maker.

**Figure 8.** The use of air quality monitoring data in the emergency response management system

Managing Emergency Response of Air Pollution by the Expert System 335

**Figure 11.** Predicted social cost of pollution in northern Taiwan

The intelligent expert system for air pollution emergency response was established in this study. The dust storm event geographical information system was studied and a knowledge-based decision support system for emergency response and risk management was established. The mathematical pattern relationship of air pollution effects on neighboring area and the corresponding response measures were included in this system. The decision maker can specify the procedure and minimize their human error in the

The performance results indicate that the function of DSGIS is acceptable. Generally speaking, DSGIS is a useful tool for taking the necessary knowledge and information about the dust storm. In addition, it also provide more convenient operating interface for the users. The concept of "map searching" is more convenient than the traditional searching methods. The performance results also show that the effectiveness of the DSGIS in searching

Since this system is designed on a module-based feature, it is easy to expand the application to more cases. Future work includes development of other independent module for

individual event and gathers more information about the event into the database.

**6. Conclusion and suggestion**

the event is reliable and acceptable.

decision process.

**Figure 9.** Example of the source distribution diagram in a county located in northern Taiwan

**Figure 10.** Predicted distribution of the pollutant concentration in northern Taiwan

**Figure 11.** Predicted social cost of pollution in northern Taiwan

#### **6. Conclusion and suggestion**

334 Air Pollution – A Comprehensive Perspective

**Figure 9.** Example of the source distribution diagram in a county located in northern Taiwan

**Figure 10.** Predicted distribution of the pollutant concentration in northern Taiwan

The intelligent expert system for air pollution emergency response was established in this study. The dust storm event geographical information system was studied and a knowledge-based decision support system for emergency response and risk management was established. The mathematical pattern relationship of air pollution effects on neighboring area and the corresponding response measures were included in this system. The decision maker can specify the procedure and minimize their human error in the decision process.

The performance results indicate that the function of DSGIS is acceptable. Generally speaking, DSGIS is a useful tool for taking the necessary knowledge and information about the dust storm. In addition, it also provide more convenient operating interface for the users. The concept of "map searching" is more convenient than the traditional searching methods. The performance results also show that the effectiveness of the DSGIS in searching the event is reliable and acceptable.

Since this system is designed on a module-based feature, it is easy to expand the application to more cases. Future work includes development of other independent module for individual event and gathers more information about the event into the database.

#### **Author details**

#### Wang-Kun Chen

*Jinwen University of Science and Technology, Department of Environment and Property Management, Taiwan*

#### **7. References**

[1] DeMers, M. N., 2000, "*Fundamentals of Geographical Information System*" 2nd ed. John Wiley and Sons, Inc. USA.

**Chapter 13**

© 2012 Ibrahim and Ilinca, licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2012 Ibrahim and Ilinca, licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**Contribution of the Compressed Air Energy**

**System**

Hussein Ibrahim and Adrian Ilinca

http://dx.doi.org/10.5772/50131

examples are as follows [1]:

regions,

households,

communities,

processing, etc.

3. By choice and method of energy provision:

**1. Introduction**

geography:

2. By energy use:

Additional information is available at the end of the chapter

**Storage in the Reduction of GHG – Case Study:**

**Application on the Remote Area Power Supply**

There are many different interpretations and classifications in use today to describe rural and/or remote areas for the purposes of discussing methods of electrification. Some useful

1. By density and concentration or clustering – setting the context of the environment or

 Small communities, villages or even towns that are remote from other habitation, Dispersed households, farms and enterprises of low density over wide areas or

Community clusters or villages surrounded by lower density dispersed

 Geographically on the same land mass, but separated by physical obstacles such as long distances, mountainous terrain, or possibly separated by water such as island

By application: household, commercial enterprise, institution, agricultural

Reticulated electricity, connected to some form of larger grid, or a local micro grid,

By power and energy (or load factor=f(energy/power)) and load profile,

Reticulated/piped fuel such as natural gas, LPG, fuel oil, diesel,

## **Contribution of the Compressed Air Energy Storage in the Reduction of GHG – Case Study: Application on the Remote Area Power Supply System**

Hussein Ibrahim and Adrian Ilinca

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/50131

#### **1. Introduction**

336 Air Pollution – A Comprehensive Perspective

Wiley and Sons, Inc. USA.

Protection Administration, Taipei, Taiwan.

National Chung-Hsin University.

atdworkshop/proceedings/ session1/campagna.pdf

[8] Zadeh L. A. (1965) "Fuzzy sets". *Information and Control* 8 (3) 338–353.

ISBN 0-471-05669-3..

& Sons, 1992.

*Jinwen University of Science and Technology, Department of Environment and Property*

[1] DeMers, M. N., 2000, "*Fundamentals of Geographical Information System*" 2nd ed. John

[2] Duda P. O., Hart P. E., Stork D. G. (2001) *Pattern Classification* (2nd), Wiley, New York,

[3] Schalkoff R., *Pattern Recognition: Statistical, Structural, and Neural Approaches*. John Wiley

[6] Turpin, R. (2004) Air Plume Modeling, Planning or Diagnostic Tool. *Environmental Protection Agency.* Retrieved February 17, 2007 From http://www.ofcm.gov/

[7] Yie C.R., 2002,"*The effect of Mainland dust storm to the acid air pollutants in central Taiwan*",

[4] Schuermann, J. (1996): *Pattern Recognition,* Wiley&Sons, 1996, ISBN 0-471-13534-8. [5] Taiwan EPA (1996) "*Air Pollution Emergency Response System*" Taiwan Environmental

**Author details**

Wang-Kun Chen

**7. References**

*Management, Taiwan*

There are many different interpretations and classifications in use today to describe rural and/or remote areas for the purposes of discussing methods of electrification. Some useful examples are as follows [1]:

	- Small communities, villages or even towns that are remote from other habitation,
	- Dispersed households, farms and enterprises of low density over wide areas or regions,
	- Community clusters or villages surrounded by lower density dispersed households,
	- Geographically on the same land mass, but separated by physical obstacles such as long distances, mountainous terrain, or possibly separated by water such as island communities,
	- By power and energy (or load factor=f(energy/power)) and load profile,
	- By application: household, commercial enterprise, institution, agricultural processing, etc.
	- Reticulated electricity, connected to some form of larger grid, or a local micro grid,
	- Reticulated/piped fuel such as natural gas, LPG, fuel oil, diesel,

Transported fuel such as natural gas, LPG, fuel oil, diesel, by land or sea transport,

Contribution of the Compressed Air Energy Storage in

the Reduction of GHG – Case Study: Application on the Remote Area Power Supply System 339

**Figure 1.** Example of a variation of diesel fuel consumption with loading

**Figure 2.** Typical load profile of a remote community [3]

 Reliance on renewable energy products such as hydro, solar photovoltaic (PV), wind, waves, tides,

The most suitable method of electricity provision (technology, business model, etc.) will usually depend on the combination of the geographic context, the consumer need, and the possibilities that are available and affordable to provide the energy requirements. Therefore, the most appropriate solutions in one place might be quite unsuitable in another [1].

Clusters and communities that are very remote from other habitation will generally be supplied by some form of centralised local generation, or via a connection to a larger but somewhat remote grid.

#### **2. Challenges related to the electrification of remote areas**

Today, diesel generators are mainly used, around the world, as emergency supply sets in telecommunication, public buildings, hospitals, or other technical installations (meteorological systems, tourist facilities, farms, etc.), and as standalone military and marine power plants, as well as the reliable isolated power source for islands or remote villages placed far from the power network [2]. In fact, there are two general methods of supplying electricity to remote areas: grid extension and the use of diesel generators. Grid extension can be very expensive in many locations. Diesel generators are therefore the only viable option for remote area electrification [3].

Classic gensets based on internal combustion engines are equipped with synchronous generators, therefore fixed speed operation is required. It gives low efficiency during low load operation (figure 1). It is not critical in emergency case operated sets, but very important in continuously operated systems, where fuel consumption is significant economic and logistic aspect. In fact, remote areas with relatively small communities generally show significant variation between the time of peak loads and the time of minimum loads. A typical example of a load profile of a remote community in Western Australia is shown below in figure 2. Diesel-powered electric generators are typically sized to meet the peak demand during the evening but must run at very low loads during "offpeak" hours during the day and night. This low-load operation results in poor fuel efficiency and increased operation and maintenance costs [3].

Moreover, low load operation of diesel genset at synchronous speed reduces the engine lifetime, by incomplete combustion of the fuel, therefore an additional dump load is required to improve the combustion process. The efficiency and fuel combustion at low load conditions can be improved by use of load adaptive adjustable speed operation of the genset [4]. In some remote locations, a dual diesel generator system is employed. When the load is light, the smaller generator is used; as the load increased, the manual switch is transferred to the larger generator. This approach results in some fuel savings, however managing this dual system is time consuming and impractical [3].

**Figure 1.** Example of a variation of diesel fuel consumption with loading

somewhat remote grid.

wind, waves, tides,

option for remote area electrification [3].

efficiency and increased operation and maintenance costs [3].

dual system is time consuming and impractical [3].

 Transported fuel such as natural gas, LPG, fuel oil, diesel, by land or sea transport, Reliance on renewable energy products such as hydro, solar photovoltaic (PV),

The most suitable method of electricity provision (technology, business model, etc.) will usually depend on the combination of the geographic context, the consumer need, and the possibilities that are available and affordable to provide the energy requirements. Therefore,

Clusters and communities that are very remote from other habitation will generally be supplied by some form of centralised local generation, or via a connection to a larger but

Today, diesel generators are mainly used, around the world, as emergency supply sets in telecommunication, public buildings, hospitals, or other technical installations (meteorological systems, tourist facilities, farms, etc.), and as standalone military and marine power plants, as well as the reliable isolated power source for islands or remote villages placed far from the power network [2]. In fact, there are two general methods of supplying electricity to remote areas: grid extension and the use of diesel generators. Grid extension can be very expensive in many locations. Diesel generators are therefore the only viable

Classic gensets based on internal combustion engines are equipped with synchronous generators, therefore fixed speed operation is required. It gives low efficiency during low load operation (figure 1). It is not critical in emergency case operated sets, but very important in continuously operated systems, where fuel consumption is significant economic and logistic aspect. In fact, remote areas with relatively small communities generally show significant variation between the time of peak loads and the time of minimum loads. A typical example of a load profile of a remote community in Western Australia is shown below in figure 2. Diesel-powered electric generators are typically sized to meet the peak demand during the evening but must run at very low loads during "offpeak" hours during the day and night. This low-load operation results in poor fuel

Moreover, low load operation of diesel genset at synchronous speed reduces the engine lifetime, by incomplete combustion of the fuel, therefore an additional dump load is required to improve the combustion process. The efficiency and fuel combustion at low load conditions can be improved by use of load adaptive adjustable speed operation of the genset [4]. In some remote locations, a dual diesel generator system is employed. When the load is light, the smaller generator is used; as the load increased, the manual switch is transferred to the larger generator. This approach results in some fuel savings, however managing this

the most appropriate solutions in one place might be quite unsuitable in another [1].

**2. Challenges related to the electrification of remote areas**

**Figure 2.** Typical load profile of a remote community [3]

Contribution of the Compressed Air Energy Storage in

the Reduction of GHG – Case Study: Application on the Remote Area Power Supply System 341

Moreover, the electricity production by the diesel is ineffective, presents significant environmental risks (spilling), contaminates the local air and largely contributes to GHG emission. In all, we estimate at 140,000 tons annual GHG emission resulting from the use of diesel generators for the subscribers of the autonomous networks in Quebec. This is

The use of diesel engines to supply power to rural communities has provided light and energy services to places where previously there has only been darkness. However, the rising cost of diesel fuel (brought on by higher oil prices and the environmental regulation for its transportation, use, and storage) combined with carbon emissions concerns is driving remote communities to look at alternative methods to supplement this power source. During the past few years, wind energy is increasingly used to reduce diesel fuel

Wind-diesel systems have been the most successfully and widely hybrid power systems applied up to date. These systems are designed to use as much as possible wind power in order to lower diesel consumption. The challenge is to keep the power quality and stability of the system besides the variability of the wind power generation and diesel operational constraints [9]. Indeed, one of the disadvantages is the intermittent nature of wind power generation. Diesel engine driven synchronous generators operating in parallel with wind turbine must maintain a good voltage and frequency regulation against active and reactive load variations and wind speed changes [10]. Integration of a storage element with diesel and wind turbine is necessary in order to get a smooth power output from a wind turbine and to optimize energy use to further reduce consumption of diesel fuel [11]. The next sections present an overview of technical challenges of wind-diesel hybrid system (WDHS), the justification of the choice of compressed air as device of energy storage to be used with WDHS and the impact of using of this storage energy system on the fuel consumption of

A wind-diesel hybrid system is any autonomous electricity generating system using wind turbine(s) with diesel generator(s) to obtain a maximum contribution by the intermittent wind resource to the total power produced, while providing continuous high quality electric power [12]. Overview of typical wind-diesel installations can be found in [13]. In the most cases, the power of installed diesel gensets is much higher than the power of wind turbines. In peak, the wind turbines can cover even more than 90% of demanded power, but in long term the fuel saving is 10-15%. The same level of fuel saving can be obtained by gensets based on power electronics, load adaptive, adjustable speed diesel without use of wind

Figure 4 presents a schematic diagram of a generalized wind diesel system. As shown, this

consumption, providing economic, environmental, social, and security benefits [8].

equivalent to GHG emitted by 35,000 cars during one year.

diesel generators and on the GHG emissions.

**3. Overview of wind-diesel hybrid system**

**3.1. Description of wind-diesel hybrid system**

turbines. It is used in light mobile power gensets [14].

system consists of the following major components:

**Figure 3.** Canadian remote communities [5]

In Canada, approximately 200,000 people live in more than 300 remote communities (Yukon, TNO, Nunavut, islands) (figure 3) and are using diesel-generated electricity, responsible for the emission of 1.2 million tons of greenhouse gases (GHG) annually [6]. In Quebec province, there are over 14,000 subscribers distributed in about forty communities not connected to the main grid. Each community constitutes an autonomous network that uses diesel generators.

In Quebec, the total production of diesel power generating units is approximately 300 GWh per year. In the meantime, the exploitation of the diesel generators is extremely expensive due to the oil price increase and transportation costs. Indeed, the communities are dependent on imported fossil fuels for most of their energy requirements. Also, there are exposed to diesel fuel price volatility, frequent fuel spills and high operation and maintenance costs including fuel transportation and bulk storage. Having said this however, in the past decade, diesel prices have more than doubled. High fuel costs have translated into tremendous increases in the cost of energy generation [3]. In Quebec for example, as the fuel should be delivered to remote locations, some of them reachable only during summer periods by barge, the cost of electricity produced by diesel generators reached in 2007 more than 50 cent/kWh in some communities, while the price for selling the electricity is established, as in the rest of Quebec, at approximately 6 cent/kWh [7]. The deficit is spread among all Quebec population as the total consumption of the autonomous grids is far from being negligible. In 2004, the autonomous networks represented 144MW of installed power, and the consumption was established at 300 GWh. Hydro-Quebec, the provincial utility, estimated at approximately 133 million CAD\$ the annual loss, resulting from the difference between the diesel electricity production cost and the uniform selling price of electricity [7].

Moreover, the electricity production by the diesel is ineffective, presents significant environmental risks (spilling), contaminates the local air and largely contributes to GHG emission. In all, we estimate at 140,000 tons annual GHG emission resulting from the use of diesel generators for the subscribers of the autonomous networks in Quebec. This is equivalent to GHG emitted by 35,000 cars during one year.

The use of diesel engines to supply power to rural communities has provided light and energy services to places where previously there has only been darkness. However, the rising cost of diesel fuel (brought on by higher oil prices and the environmental regulation for its transportation, use, and storage) combined with carbon emissions concerns is driving remote communities to look at alternative methods to supplement this power source. During the past few years, wind energy is increasingly used to reduce diesel fuel consumption, providing economic, environmental, social, and security benefits [8].

Wind-diesel systems have been the most successfully and widely hybrid power systems applied up to date. These systems are designed to use as much as possible wind power in order to lower diesel consumption. The challenge is to keep the power quality and stability of the system besides the variability of the wind power generation and diesel operational constraints [9]. Indeed, one of the disadvantages is the intermittent nature of wind power generation. Diesel engine driven synchronous generators operating in parallel with wind turbine must maintain a good voltage and frequency regulation against active and reactive load variations and wind speed changes [10]. Integration of a storage element with diesel and wind turbine is necessary in order to get a smooth power output from a wind turbine and to optimize energy use to further reduce consumption of diesel fuel [11]. The next sections present an overview of technical challenges of wind-diesel hybrid system (WDHS), the justification of the choice of compressed air as device of energy storage to be used with WDHS and the impact of using of this storage energy system on the fuel consumption of diesel generators and on the GHG emissions.

#### **3. Overview of wind-diesel hybrid system**

340 Air Pollution – A Comprehensive Perspective

**Figure 3.** Canadian remote communities [5]

In Canada, approximately 200,000 people live in more than 300 remote communities (Yukon, TNO, Nunavut, islands) (figure 3) and are using diesel-generated electricity, responsible for the emission of 1.2 million tons of greenhouse gases (GHG) annually [6]. In Quebec province, there are over 14,000 subscribers distributed in about forty communities not connected to the main grid. Each community constitutes an autonomous network that uses diesel generators.

In Quebec, the total production of diesel power generating units is approximately 300 GWh per year. In the meantime, the exploitation of the diesel generators is extremely expensive due to the oil price increase and transportation costs. Indeed, the communities are dependent on imported fossil fuels for most of their energy requirements. Also, there are exposed to diesel fuel price volatility, frequent fuel spills and high operation and maintenance costs including fuel transportation and bulk storage. Having said this however, in the past decade, diesel prices have more than doubled. High fuel costs have translated into tremendous increases in the cost of energy generation [3]. In Quebec for example, as the fuel should be delivered to remote locations, some of them reachable only during summer periods by barge, the cost of electricity produced by diesel generators reached in 2007 more than 50 cent/kWh in some communities, while the price for selling the electricity is established, as in the rest of Quebec, at approximately 6 cent/kWh [7]. The deficit is spread among all Quebec population as the total consumption of the autonomous grids is far from being negligible. In 2004, the autonomous networks represented 144MW of installed power, and the consumption was established at 300 GWh. Hydro-Quebec, the provincial utility, estimated at approximately 133 million CAD\$ the annual loss, resulting from the difference between the diesel electricity production cost and the uniform selling price of electricity [7].

#### **3.1. Description of wind-diesel hybrid system**

A wind-diesel hybrid system is any autonomous electricity generating system using wind turbine(s) with diesel generator(s) to obtain a maximum contribution by the intermittent wind resource to the total power produced, while providing continuous high quality electric power [12]. Overview of typical wind-diesel installations can be found in [13]. In the most cases, the power of installed diesel gensets is much higher than the power of wind turbines. In peak, the wind turbines can cover even more than 90% of demanded power, but in long term the fuel saving is 10-15%. The same level of fuel saving can be obtained by gensets based on power electronics, load adaptive, adjustable speed diesel without use of wind turbines. It is used in light mobile power gensets [14].

Figure 4 presents a schematic diagram of a generalized wind diesel system. As shown, this system consists of the following major components:

Contribution of the Compressed Air Energy Storage in

the Reduction of GHG – Case Study: Application on the Remote Area Power Supply System 343