**3. Studies of aerosol in the GMA**

#### **3.1. Concentration of PM**

The average concentrations of PM2.5 in Centro and Miravalle exceeded the annual limits during the periods of January–December 2007, January–June 2008, and April 2009–January 2010 (as a whole the three periods represent 27 months of sampling, justifying its comparison with the annual limit of the standard of 15 μg m−3 ). In Miravalle, concentrations of PM2.5 were higher during 2007, probably due to higher industrial activity and the natural barriers in that area that prevent the evacuation of pollutants; in Centro and Miravalle were between 2.9 and 3.5 times higher than the annual limit of the standard, respectively. At the Miravalle site, the higher levels of PM were evident in the dry seasons (warm and cold) when compared to the ones measured in the rainy season. In general, during the cold period, particle levels are increased by low temperatures, unfavorable conditions for the dispersion of pollutants, by the increase in the consumption of fossil fuels, and because the conversion of gases to particles is favored [18]. The lowest temperatures were recorded in February 2008; they occurred particularly in Miravalle, and it is likely that the processes already mentioned were related to the high concentrations of PM2.5.

sulfates and nitrates [33]. For the 2009–2010 period, Centro and Miravalle sites did not show differences in their particle concentrations; only in Miravalle were observed higher levels dur-

Air Pollution in an Urban Area of Mexico: Sources of Emission (Vehicular, Natural, Industrial,…

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59

**Figure 1.** Map of monitoring stations (gray circles) into GMA, the red circles indicate the sampling sites.

Other studies considered PM2.5 measurements at shorter sampling periods and the values

Miravalle in May–June 2009, and Centro and Tlaquepaque in March–May 2014, showed concentrations close to this limit, but not exceeding it. It should be mentioned that the levels of this same particle size in the studies carried out at the end of 2014 were compared with the

[19] without exceeding the limit in any case.

PM10 levels in the area of the Environmental Fragility Polygon (POFA, by the acronym in

health of the local population. Likewise, concentrations of PM10 were 4–10 times higher than those of PM2.5 (in their daily levels), indicating the domain of emission sources of coarse particles. The low correlation between fine and coarse particles suggests different emission sources.

impacts as done before. However, Centro and Tlaquepaque sites seem to have equal PM1 concentrations, despite the characteristics of each of them concerning the types of emission sources, so that further studies on this particle size will be needed in other parts of the city to

establish relationships. It is important to note that the concentrations of PM1

levels, there is no Mexican regulation. It is impossible to conclude about the

, [34]). Only sites like

are recent and

), which suggests a risk for the

contrasted against the standard limit for a period of 24 h (65 μg m−3

Spanish) exceeded 1.6 times the standard for 24 h (120 μg m−3

ing the cold dry season against the rainy season.

the first for this type of pollutants in the GMA.

24 h standard of 45 μg m−3

Regarding PM1

Other studies considered PM2.5 measurements at shorter sampling periods and the values contrasted against the standard limit for a period of 24 h (65 μg m−3 , [19]). Only sites like Miravalle in May–June 2009, and Centro and Tlaquepaque in March–May 2014, showed concentrations close to this limit, but not exceeding it. It should be mentioned that the levels of this same particle size in the studies carried out at the end of 2014 were compared with the 24 h standard of 45 μg m−3 [19] without exceeding the limit in any case (**Figure 1**).

PM10 levels in the area of the Environmental Fragility Polygon (POFA, by the acronym in Spanish) exceeded 1.6 times the standard for 24 h (120 μg m−3 ), which suggests a risk for the health of the local population. Likewise, concentrations of PM10 were 4–10 times higher than those of PM2.5 (in their daily levels), indicating the domain of emission sources of coarse particles. The low correlation between fine and coarse particles suggests different emission sources (**Table 1**).

Another environmental factor, the rain, contributes to the temporal variation of the particles, which are grounded by direct drag or because air humidity induces their growth in size and precipitation. Some hygroscopic compounds favor this phenomenon in the particles, such as Air Pollution in an Urban Area of Mexico: Sources of Emission (Vehicular, Natural, Industrial,… http://dx.doi.org/10.5772/intechopen.80000 59

About the environmental impacts, the low visibility (an optical indicator of air quality) is attributed mainly to the dispersion and absorption of visible light by particles in the atmosphere [15] with diameters mostly between 0.1 and 1.0 μm. The fine particles scatter visible light much more effectively per unit mass since their size is comparable to the wavelength of visible light [16]. Additionally, damage to ecosystems can also be mentioned that can result in the reduction of biodiversity and loss of environmental goods

The average concentrations of PM2.5 in Centro and Miravalle exceeded the annual limits during the periods of January–December 2007, January–June 2008, and April 2009–January 2010 (as a whole the three periods represent 27 months of sampling, justifying its comparison with

higher during 2007, probably due to higher industrial activity and the natural barriers in that area that prevent the evacuation of pollutants; in Centro and Miravalle were between 2.9 and 3.5 times higher than the annual limit of the standard, respectively. At the Miravalle site, the higher levels of PM were evident in the dry seasons (warm and cold) when compared to the ones measured in the rainy season. In general, during the cold period, particle levels are increased by low temperatures, unfavorable conditions for the dispersion of pollutants, by the increase in the consumption of fossil fuels, and because the conversion of gases to particles is favored [18]. The lowest temperatures were recorded in February 2008; they occurred particularly in Miravalle, and it is likely that the processes already mentioned were related to the

Other studies considered PM2.5 measurements at shorter sampling periods and the values

Miravalle in May–June 2009, and Centro and Tlaquepaque in March–May 2014, showed concentrations close to this limit, but not exceeding it. It should be mentioned that the levels of this same particle size in the studies carried out at the end of 2014 were compared with the

PM10 levels in the area of the Environmental Fragility Polygon (POFA, by the acronym in

the health of the local population. Likewise, concentrations of PM10 were 4–10 times higher than those of PM2.5 (in their daily levels), indicating the domain of emission sources of coarse particles. The low correlation between fine and coarse particles suggests different emission

Another environmental factor, the rain, contributes to the temporal variation of the particles, which are grounded by direct drag or because air humidity induces their growth in size and precipitation. Some hygroscopic compounds favor this phenomenon in the particles, such as

[19] without exceeding the limit in any case (**Figure 1**).

contrasted against the standard limit for a period of 24 h (65 μg m−3

Spanish) exceeded 1.6 times the standard for 24 h (120 μg m−3

). In Miravalle, concentrations of PM2.5 were

, [19]). Only sites like

), which suggests a risk for

and services [17].

**3.1. Concentration of PM**

high concentrations of PM2.5.

24 h standard of 45 μg m−3

sources (**Table 1**).

**3. Studies of aerosol in the GMA**

58 Air Pollution - Monitoring, Quantification and Removal of Gases and Particles

the annual limit of the standard of 15 μg m−3

**Figure 1.** Map of monitoring stations (gray circles) into GMA, the red circles indicate the sampling sites.

sulfates and nitrates [33]. For the 2009–2010 period, Centro and Miravalle sites did not show differences in their particle concentrations; only in Miravalle were observed higher levels during the cold dry season against the rainy season.

Other studies considered PM2.5 measurements at shorter sampling periods and the values contrasted against the standard limit for a period of 24 h (65 μg m−3 , [34]). Only sites like Miravalle in May–June 2009, and Centro and Tlaquepaque in March–May 2014, showed concentrations close to this limit, but not exceeding it. It should be mentioned that the levels of this same particle size in the studies carried out at the end of 2014 were compared with the 24 h standard of 45 μg m−3 [19] without exceeding the limit in any case.

PM10 levels in the area of the Environmental Fragility Polygon (POFA, by the acronym in Spanish) exceeded 1.6 times the standard for 24 h (120 μg m−3 ), which suggests a risk for the health of the local population. Likewise, concentrations of PM10 were 4–10 times higher than those of PM2.5 (in their daily levels), indicating the domain of emission sources of coarse particles. The low correlation between fine and coarse particles suggests different emission sources.

Regarding PM1 levels, there is no Mexican regulation. It is impossible to conclude about the impacts as done before. However, Centro and Tlaquepaque sites seem to have equal PM1 concentrations, despite the characteristics of each of them concerning the types of emission sources, so that further studies on this particle size will be needed in other parts of the city to establish relationships. It is important to note that the concentrations of PM1 are recent and the first for this type of pollutants in the GMA.

**3.2. Heavy metals and toxic elements**

**3.3. Anions and cations**

and NH3

NOx

case of sulfate, this is a derivate from SO2

The objective of these studies was also to generate reliable information about the possible sources of emission into the atmosphere, essential to establish actions that could reduce current levels of pollution in the ambient air and to even be able to estimate the degree of exposure of the human population. From the study of PM2.5 (2007) and its elemental composition in Centro and Miravalle,

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61

The EF must be understood as a relation of the elements of interest with an abundant element of the soil (usually Fe and K), but also present in the particles. An EF > 5 suggests an anthropogenic source (different to the geological or natural origin). In Centro and Miravalle, Pb, Cd, Cr, Cu, Mo, Sb, Se, Tl, and Zn, as well as Ni and V in Centro had high FE, suggesting that they came from anthropogenic sources. On the other hand, Co, Fe, Mg, Ca, Mn, Sr, and Ti, clearly showed evidence from resuspension of dust either by the winds or by vehicular traffic (natural origin). For the study of January–June 2008, the most abundant elements in both sites were Fe, Ti, Zn, Mg, and Pb. Fe, Ti, and Mg showed FE values that indicate a geological origin, while Zn and Pb probably are incorporated into the atmosphere by vehicle emissions and the use of these elements in non-ferrous alloys [35, 36]. The elemental composition profile of this study was related to the PM2.5 elementary profiles of the international USEPA SPECIATE database (version 4.2). This database gathers scientific data from the profiles of specific particle sources. The profiles of Miravalle and Centro coincided with the profiles of Paved Road Dust (PRD), Diesel Exhaust (DE) and industrial soil (IS) (r2 = 94%, r2 = 72%, r2 = 68%, and r2 = 89%, r2 = 75%, r2 = 66%, respectively). Miravalle, in addition to the high industrial activity, has an intense flow of heavy vehicles which can be the origin of the resuspension of particles, phenomenon increased by large land areas without vegetation during the dry-season months. In Centro, the coincidence with the profiles of PRD and IS could be understood by the phenomenon of particle resuspension, or by the transport of emissions from nearby places with some industrial activity. In both

emission sources were established through the estimation of the enrichment factors (EF).

sites, the DE profile was influenced by public transportation that works with diesel.

During 2009, some trends were consistent with those observed in 2008 regarding the abundance of those elements related to PM2.5. It is highlighted that the Fe contributed 60 and 72% to the total elemental mass in Centro and Miravalle, respectively. In this way, it can be seen how different emission sources, whether natural or anthropogenic, could affect the composition of the PM2.5 particles, and at the same time give indications of the specific emission sources.

From the study in 2009, sulfate, nitrate, and ammonium were the more abundant compounds in Centro and Miravalle; sulfate and calcium showed high concentrations in Miravalle. In the

the burning of fossil fuels with sulfur content. While nitrates and ammonium are formed from

such as gasoline and diesel in the GMA [37, 38]. These results suggest that the relatively constant concentrations of sulfate and nitrate in both sites during the year are because the levels emitted from parent gases are similar. For sulfates during winter, for example, the precursor gas is at high concentrations, while during the summer, the high temperature and high solar

(ammonia), the latter gas is emitted mainly by the vehicle fleet that uses fuels

which comes from direct industrial emissions and

1 Corresponds to the medians of the sampling periods.

2 in ng m−3 .

\*Acronym in Spanish.

\*\*For this study, the sampling sites were Centro and CIATEJ.

\*\*\*Corresponds to the period of January–August, 2008.

\*\*\*\*The chemical species mentioned were only determined in PM10.

**Table 1.** GMA aerosols research.

#### **3.2. Heavy metals and toxic elements**

**Sampling period Sites Size Average**

60 Air Pollution - Monitoring, Quantification and Removal of Gases and Particles

Miravalle

Miravalle

Miravalle

Apr 6th–18th, 2013 POFA\* PM10

Centro Tlaquepaque Centro Las Águilas

Loma Dorada Las Pintas Las Águilas Centro

Tlaquepaque

\*\*For this study, the sampling sites were Centro and CIATEJ. \*\*\*Corresponds to the period of January–August, 2008.

\*\*\*\*The chemical species mentioned were only determined in PM10.

Corresponds to the medians of the sampling periods.

Mar–May, 2014 July–Sep, 2014

Aug 24th–30th, 2014 Oblatos

Apr–June, 2015 Centro

**Table 1.** GMA aerosols research.

1

2 in ng m−3 . \*Acronym in Spanish.

Centro Miravallle Centro Miravalle AUG Centro Miravalle AUG

Jan–Dec, 2007 Centro

Jan–Dec, 2008 Centro

Apr 2009–Jan 2010 Centro

May 25th-June 6th,

2009

**(μg m−3 )**

[22]

PM2.5 44.10 52.80

PM2.5 59.09 81.36

> 39.30 58.00 21.00 37.00 21.00 46.00 79.00 43.00

PM2.5

PM2.5

PM10

PM2.5

187.83 29.33

PM2.5 57.071,2 61.681,2 28.971,2 28.231,2

PM2.5 22.67 15.67 30.00 17.33 17.00

PM1 30.922 21.802

PM2.5 33.001,2 45.001,2 **Chemical compounds NOM-025-SSA1**

Heavy metals and toxic elements[20]; anions & cations [21]; black carbon\*\*

Anions & cations [23]; heavy metals and toxic elements [22], black carbon\*\*\* [24]

Heavy metals and toxic elements;

Heavy metals and toxic elements; anions & cations; black carbon [26]

anions & cations [25].

Oxy-PAHs [27]

Anions & cations\*\*\*\* [28]

PAHs & quinones [29, 30]

Black carbon & VOCs [31]

PAHs & quinones [32]

**1993 and 2014**

NOM-025- SSA1-1993 PM10 120 μg m−3

50 μg m−3

15 μg m−3

NOM-025- SSA1-2014 PM10 75 μg m−3

40 μg m−3

12 μg m−3

PM2.5 45 μg m−3

PM2.5 65 μg m−3 (24 h)

(annual)

(24 h)

(24 h)

(24 h)

(annual)

(annual)

(annual)

The objective of these studies was also to generate reliable information about the possible sources of emission into the atmosphere, essential to establish actions that could reduce current levels of pollution in the ambient air and to even be able to estimate the degree of exposure of the human population. From the study of PM2.5 (2007) and its elemental composition in Centro and Miravalle, emission sources were established through the estimation of the enrichment factors (EF).

The EF must be understood as a relation of the elements of interest with an abundant element of the soil (usually Fe and K), but also present in the particles. An EF > 5 suggests an anthropogenic source (different to the geological or natural origin). In Centro and Miravalle, Pb, Cd, Cr, Cu, Mo, Sb, Se, Tl, and Zn, as well as Ni and V in Centro had high FE, suggesting that they came from anthropogenic sources. On the other hand, Co, Fe, Mg, Ca, Mn, Sr, and Ti, clearly showed evidence from resuspension of dust either by the winds or by vehicular traffic (natural origin).

For the study of January–June 2008, the most abundant elements in both sites were Fe, Ti, Zn, Mg, and Pb. Fe, Ti, and Mg showed FE values that indicate a geological origin, while Zn and Pb probably are incorporated into the atmosphere by vehicle emissions and the use of these elements in non-ferrous alloys [35, 36]. The elemental composition profile of this study was related to the PM2.5 elementary profiles of the international USEPA SPECIATE database (version 4.2). This database gathers scientific data from the profiles of specific particle sources. The profiles of Miravalle and Centro coincided with the profiles of Paved Road Dust (PRD), Diesel Exhaust (DE) and industrial soil (IS) (r2 = 94%, r2 = 72%, r2 = 68%, and r2 = 89%, r2 = 75%, r2 = 66%, respectively). Miravalle, in addition to the high industrial activity, has an intense flow of heavy vehicles which can be the origin of the resuspension of particles, phenomenon increased by large land areas without vegetation during the dry-season months. In Centro, the coincidence with the profiles of PRD and IS could be understood by the phenomenon of particle resuspension, or by the transport of emissions from nearby places with some industrial activity. In both sites, the DE profile was influenced by public transportation that works with diesel.

During 2009, some trends were consistent with those observed in 2008 regarding the abundance of those elements related to PM2.5. It is highlighted that the Fe contributed 60 and 72% to the total elemental mass in Centro and Miravalle, respectively. In this way, it can be seen how different emission sources, whether natural or anthropogenic, could affect the composition of the PM2.5 particles, and at the same time give indications of the specific emission sources.

### **3.3. Anions and cations**

From the study in 2009, sulfate, nitrate, and ammonium were the more abundant compounds in Centro and Miravalle; sulfate and calcium showed high concentrations in Miravalle. In the case of sulfate, this is a derivate from SO2 which comes from direct industrial emissions and the burning of fossil fuels with sulfur content. While nitrates and ammonium are formed from NOx and NH3 (ammonia), the latter gas is emitted mainly by the vehicle fleet that uses fuels such as gasoline and diesel in the GMA [37, 38]. These results suggest that the relatively constant concentrations of sulfate and nitrate in both sites during the year are because the levels emitted from parent gases are similar. For sulfates during winter, for example, the precursor gas is at high concentrations, while during the summer, the high temperature and high solar radiation favor the oxidation of the precursor and increase the formation of sulfate [39]. This study was complemented by an ion balance, which established the existence of neutralization processes between the chemical species of the particles. For Centro, the presence of slightly acidic particles was determined, while in Miravalle, the particles showed an alkaline nature. The acid form of sulfates can influence the bioavailability of some metals, increase the toxicity of the particles [40], augment the adsorption of compounds into the particles, or create hygroscopic environments in them, all of which favor the solubility of some gases, promoting that they can reach deep areas of the respiratory tract [41], increasing the deposition of toxic compounds in the lungs. In the case of alkalinity, the potential effects on human health from exposure to particles with this property are now not very clear [42].

On the other hand, during 2008, sulfate, nitrate, and ammonium compounds showed the highest levels, without differences between sites, suggesting sources and formation processes in common, particularly in the southwest of the GMA. In Centro, the ion mass represented 46% of the mass of the PM2.5, while in Miravalle, they did not exceed 28%. Studies like [43] indicate that the mass of these three ion species contributed with 40% and up to 65% to the mass of PM2.5. The results on the contribution of the major inorganic ions to PM2.5 can serve as a basis for establishing the connection between fine particles and possible adverse impacts on human health.

The short study on ion species also in 2009 in the two sites mentioned above, coincide in sulfate, nitrate, and ammonium as those of higher abundance in the previous works, but also highlight the presence of calcium and potassium. An impressive result was to find higher levels of nitrates concerning sulfates and ammonium. In Miravalle, nitrate represented 68% of the mass of total ions, while in Centro it was 57%. The abundance of these ions shows the critical role that secondary sources have in the formation of fine particles.

change by affecting the intensity of solar radiation [44]. Due to the porosity of the BC particles and their enormous surface area, they can adsorb significant amounts of mutagenic and carcinogenic organic compounds [45]. Therefore, BC is a risk to human health due to its ability to

**Figure 2.** Average monthly concentration of different species of anions and cations associated with PM2.5 of Miravalle during 2007. Sulfate, nitrate, and ammonium are the species that contribute most to the concentrations of fine particles

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63

During 2007, BC associated with PM2.5 was monitored in Centro and CIATEJ [22]. Overall, low temperatures and unfavorable conditions for the dispersion of pollutants resulted in high concentrations at both sites (November and December). During July and August, a significant reduction of this pollutant was observed (factor 1:0.5 or 1:0.3). The presence of humidity and the intensity of the winds that generate vertical movements of air masses may also favor the

Furthermore, BC concentrations during 24 h periods were studied. In the morning, between 8 and 9, the highest level of concentration was observed during all months of the year (the magnitude of this peak is high or low, depending on the months of the dry or rainy season). This phenomenon occurred in both places, but with higher levels in Centro. During evenings, both sites showed a second-high level of BC concentrations, and this can be seen between 20 and 21 h. With some exceptions, the concentrations of this last event do not exceed the levels observed during the morning. **Figure 3** shows the trends and variations of BC concentrations throughout the day. The abovementioned results are due to the increased population activities during the mornings, mainly because of the displacement from home to job, which implies a higher use of the public transport and private vehicles. Around midday not only the intensity of this activity of the population is reduced, but also better environmental conditions for the dispersion of

BC dispersion, altogether with the frequency and severity of rainfall.

carry carcinogens to lungs [46].

throughout the year.

The last study about species of ions and particles (PM10) was carried out on three different sites within the POFA area (on specific days of April 2013) [28]. In this area pollution problems are recurrent in the soil, water, and air because it has changed land use, environmental deterioration, loss of its original fauna and flora. The low air quality due to particles, ozone, and the presence of unpleasant odors is the most severe environmental problem in the POFA. Species such as sulfate, nitrate, chloride, calcium, sodium, and ammonium were the most abundant, without differences in their concentrations between sites (except chloride), which suggest the probable homogeneity of their levels in the area of study (**Figure 2**).

The total ions contributed with 15% of the PM10 mass (other components such as organic matter, elemental carbon, and mineral silica could integrate the particles). Calcium and sulfate showed significant correlations with PM10 (r = 0.70 and r = 0.64, respectively), evidence of emission sources of coarse particles in the POFA. Also, chloride could derive from local industry, burning biomass, and burning of agricultural waste. Finally, the PM10 collected in this study had an acidic nature; this is based on the ratio of total sulfate and total ammonium.

#### **3.4. Black carbon**

Black carbon (BC), or elemental carbon, comes from incomplete combustion of diesel vehicles, electric power plants and various industrial processes, wood burning and forest fires. BC plays an important role due to its porous nature and absorption, as well as in global climate Air Pollution in an Urban Area of Mexico: Sources of Emission (Vehicular, Natural, Industrial,… http://dx.doi.org/10.5772/intechopen.80000 63

radiation favor the oxidation of the precursor and increase the formation of sulfate [39]. This study was complemented by an ion balance, which established the existence of neutralization processes between the chemical species of the particles. For Centro, the presence of slightly acidic particles was determined, while in Miravalle, the particles showed an alkaline nature. The acid form of sulfates can influence the bioavailability of some metals, increase the toxicity of the particles [40], augment the adsorption of compounds into the particles, or create hygroscopic environments in them, all of which favor the solubility of some gases, promoting that they can reach deep areas of the respiratory tract [41], increasing the deposition of toxic compounds in the lungs. In the case of alkalinity, the potential effects on human health from

On the other hand, during 2008, sulfate, nitrate, and ammonium compounds showed the highest levels, without differences between sites, suggesting sources and formation processes in common, particularly in the southwest of the GMA. In Centro, the ion mass represented 46% of the mass of the PM2.5, while in Miravalle, they did not exceed 28%. Studies like [43] indicate that the mass of these three ion species contributed with 40% and up to 65% to the mass of PM2.5. The results on the contribution of the major inorganic ions to PM2.5 can serve as a basis for establishing the connection between fine particles and possible adverse impacts on human health.

The short study on ion species also in 2009 in the two sites mentioned above, coincide in sulfate, nitrate, and ammonium as those of higher abundance in the previous works, but also highlight the presence of calcium and potassium. An impressive result was to find higher levels of nitrates concerning sulfates and ammonium. In Miravalle, nitrate represented 68% of the mass of total ions, while in Centro it was 57%. The abundance of these ions shows the

The last study about species of ions and particles (PM10) was carried out on three different sites within the POFA area (on specific days of April 2013) [28]. In this area pollution problems are recurrent in the soil, water, and air because it has changed land use, environmental deterioration, loss of its original fauna and flora. The low air quality due to particles, ozone, and the presence of unpleasant odors is the most severe environmental problem in the POFA. Species such as sulfate, nitrate, chloride, calcium, sodium, and ammonium were the most abundant, without differences in their concentrations between sites (except chloride), which suggest the

The total ions contributed with 15% of the PM10 mass (other components such as organic matter, elemental carbon, and mineral silica could integrate the particles). Calcium and sulfate showed significant correlations with PM10 (r = 0.70 and r = 0.64, respectively), evidence of emission sources of coarse particles in the POFA. Also, chloride could derive from local industry, burning biomass, and burning of agricultural waste. Finally, the PM10 collected in this study had an acidic nature; this is based on the ratio of total sulfate and total ammonium.

Black carbon (BC), or elemental carbon, comes from incomplete combustion of diesel vehicles, electric power plants and various industrial processes, wood burning and forest fires. BC plays an important role due to its porous nature and absorption, as well as in global climate

exposure to particles with this property are now not very clear [42].

62 Air Pollution - Monitoring, Quantification and Removal of Gases and Particles

critical role that secondary sources have in the formation of fine particles.

probable homogeneity of their levels in the area of study (**Figure 2**).

**3.4. Black carbon**

**Figure 2.** Average monthly concentration of different species of anions and cations associated with PM2.5 of Miravalle during 2007. Sulfate, nitrate, and ammonium are the species that contribute most to the concentrations of fine particles throughout the year.

change by affecting the intensity of solar radiation [44]. Due to the porosity of the BC particles and their enormous surface area, they can adsorb significant amounts of mutagenic and carcinogenic organic compounds [45]. Therefore, BC is a risk to human health due to its ability to carry carcinogens to lungs [46].

During 2007, BC associated with PM2.5 was monitored in Centro and CIATEJ [22]. Overall, low temperatures and unfavorable conditions for the dispersion of pollutants resulted in high concentrations at both sites (November and December). During July and August, a significant reduction of this pollutant was observed (factor 1:0.5 or 1:0.3). The presence of humidity and the intensity of the winds that generate vertical movements of air masses may also favor the BC dispersion, altogether with the frequency and severity of rainfall.

Furthermore, BC concentrations during 24 h periods were studied. In the morning, between 8 and 9, the highest level of concentration was observed during all months of the year (the magnitude of this peak is high or low, depending on the months of the dry or rainy season). This phenomenon occurred in both places, but with higher levels in Centro. During evenings, both sites showed a second-high level of BC concentrations, and this can be seen between 20 and 21 h. With some exceptions, the concentrations of this last event do not exceed the levels observed during the morning. **Figure 3** shows the trends and variations of BC concentrations throughout the day.

The abovementioned results are due to the increased population activities during the mornings, mainly because of the displacement from home to job, which implies a higher use of the public transport and private vehicles. Around midday not only the intensity of this activity of the population is reduced, but also better environmental conditions for the dispersion of

emissions (gases and particles), could be responsible for a large part of the pollution problems in urban areas of our country; this is the case of artisan brick producers. In Mexico, Jalisco is the second state with the largest number of artisan brick producers, 2500 for 2012, 46% of which are in the GMA. The artisan brick industry contributes significantly to the emission of

Air Pollution in an Urban Area of Mexico: Sources of Emission (Vehicular, Natural, Industrial,…

should be noted that a significant fraction of these particles, especially PM2.5, corresponds to BC [50], another relevant reason for their study. The scarce technology and standardization in the artisan brick production is a constant not only locally, but also in the country. Although there are numerous variants of kiln types designed for this productive activity (such as the MK2 one), the most common are the field campaign kiln and the fixed-walls kiln [51]. Few of these kilns (including the MK2 one) have additional emission control systems to reduce the gases and emitted particles. In this sense, it is possible to minimize particles dragging by water curtains, being one of the most straightforward and most efficient principles [52], although it does not act in the same way as the emission gases (the absorption of gases is sensitive to the increase in the temperature of the washing water). Our research group has observed the behavior of particles removal during the process of brick cooking when using recirculated washing water in an MK2 kiln, finding increasing water turbidity with time. The change in turbidity and its level (583 ± 16.7, FTU) is related to the number of total solids

the efficiency of reducing emissions to the atmosphere [53]. As part of these observations, a significant increase in the temperature of the washing water was also measured (up to 52°C),

most abundant gas). Both of the washing water properties change with time up to the final

water of this process could experience a physical-chemical treatment due to the ratio BOD/

As regards the organic matter associated with the particles, only a small percentage of groups of compounds have been characterized, such as polycyclic aromatic hydrocarbons (PAHs), oxygenated polycyclic aromatic hydrocarbons (oxy-PAHs), among others [54]. The identification and quantification of PAHs, a diverse group of complex compounds with two or more fused benzene rings, has been intensively investigated [55]. In the atmosphere, PAHs can be present in particles (adsorb/absorb on their surface) or in the gas phase. It is expected that high molecular weight PAHs can be found mainly in the fine particles [56] because they have a more upper specific surface area and therefore greater adsorption than coarse particles [57]. The United States Environmental Protection Agency (USEPA) has defined 16 PAHs as a priority due to the known carcinogenic and mutagenic properties of them. It is important to note that only the European Union considers the regulation of PAHs in ambient air expressed in benzo[a]

as well as a decrease in pH (3.45) due to the absorption of part of the CO2

COD = 0.17 because the BOD level is low compared to other types of wastewater.

pyrene concentration (the carcinogen of reference by the IARC) at levels of 1 ng m−3

An essential aspect of PAHs is that they can be decomposed by photodegradation, chemical reactions with others pollutants in the urban environment, or gas-particle partition.

**3.5. Organic components as emission indicators (polycyclic aromatic hydrocarbons**

) therein which are removed by the washing water, thus contributing to

, CO, and NO not exceeding 26, 37, and 22%, respectively. The washing

, NH3

http://dx.doi.org/10.5772/intechopen.80000

, among others. It

65

emissions (the

per year [58].

particulate (PM2.5 and PM10) and gaseous pollutants such as CO, CO2

(827 ± 62.38, mg L−1

limit removal of CO2

**and oxy-PAHs)**

**Figure 3.** Hourly variation of BC concentrations per season in the Centro site during 2008. DS1—corresponds from January to May; RS—corresponds from June to October; DS2—corresponds from November to December.

pollutants are favored, thus generating lower concentrations of BC. The increase in BC at night is due to vehicles circulation related to people returning home after workday.

The BC data from the Centro could be correlated with the environmental concentrations of criteria pollutants measured at the same site. It is highlighted that with CO, NO2 , and SO2 the correlations were high, but not in the case of ozone (a contaminant of secondary origin). These results support the primary origin of BC (mainly diesel vehicles). The second study on BC was conducted between January and August 2008, in addition to being carried out along with a manual sampling of PM2.5; both pollutants were monitored and sampled at Centro and Miravalle sites. The results confirm some of the trends described for the previous year of monitoring (variations throughout the day and time when the concentration increase due to peaks in population activities).

An interesting contribution of this last study was the estimation of the contribution that BC makes to the PM2.5, less than 10%. This value is similar to that estimated in other cities such as City of Mexico, Santiago de Chile, Helsinki, among others [47–49]. In Centro and Miravalle, a moderate relation was found between the concentrations of BC and CO (carbon monoxide) and confirms a common source of emission (burning of biomass and petroleum fuels).

Regarding BC emissions, those sources that use organic matter (burning biomass) as fuel during simple combustion processes and that lack of technological equipment to control their emissions (gases and particles), could be responsible for a large part of the pollution problems in urban areas of our country; this is the case of artisan brick producers. In Mexico, Jalisco is the second state with the largest number of artisan brick producers, 2500 for 2012, 46% of which are in the GMA. The artisan brick industry contributes significantly to the emission of particulate (PM2.5 and PM10) and gaseous pollutants such as CO, CO2 , NH3 , among others. It should be noted that a significant fraction of these particles, especially PM2.5, corresponds to BC [50], another relevant reason for their study. The scarce technology and standardization in the artisan brick production is a constant not only locally, but also in the country. Although there are numerous variants of kiln types designed for this productive activity (such as the MK2 one), the most common are the field campaign kiln and the fixed-walls kiln [51]. Few of these kilns (including the MK2 one) have additional emission control systems to reduce the gases and emitted particles. In this sense, it is possible to minimize particles dragging by water curtains, being one of the most straightforward and most efficient principles [52], although it does not act in the same way as the emission gases (the absorption of gases is sensitive to the increase in the temperature of the washing water). Our research group has observed the behavior of particles removal during the process of brick cooking when using recirculated washing water in an MK2 kiln, finding increasing water turbidity with time. The change in turbidity and its level (583 ± 16.7, FTU) is related to the number of total solids (827 ± 62.38, mg L−1 ) therein which are removed by the washing water, thus contributing to the efficiency of reducing emissions to the atmosphere [53]. As part of these observations, a significant increase in the temperature of the washing water was also measured (up to 52°C), as well as a decrease in pH (3.45) due to the absorption of part of the CO2 emissions (the most abundant gas). Both of the washing water properties change with time up to the final limit removal of CO2 , CO, and NO not exceeding 26, 37, and 22%, respectively. The washing water of this process could experience a physical-chemical treatment due to the ratio BOD/ COD = 0.17 because the BOD level is low compared to other types of wastewater.

#### **3.5. Organic components as emission indicators (polycyclic aromatic hydrocarbons and oxy-PAHs)**

**Figure 3.** Hourly variation of BC concentrations per season in the Centro site during 2008. DS1—corresponds from

pollutants are favored, thus generating lower concentrations of BC. The increase in BC at

The BC data from the Centro could be correlated with the environmental concentrations of

the correlations were high, but not in the case of ozone (a contaminant of secondary origin). These results support the primary origin of BC (mainly diesel vehicles). The second study on BC was conducted between January and August 2008, in addition to being carried out along with a manual sampling of PM2.5; both pollutants were monitored and sampled at Centro and Miravalle sites. The results confirm some of the trends described for the previous year of monitoring (variations throughout the day and time when the concentration increase due to

An interesting contribution of this last study was the estimation of the contribution that BC makes to the PM2.5, less than 10%. This value is similar to that estimated in other cities such as City of Mexico, Santiago de Chile, Helsinki, among others [47–49]. In Centro and Miravalle, a moderate relation was found between the concentrations of BC and CO (carbon monoxide) and confirms a common source of emission (burning of biomass and petroleum fuels).

Regarding BC emissions, those sources that use organic matter (burning biomass) as fuel during simple combustion processes and that lack of technological equipment to control their

, and SO2

January to May; RS—corresponds from June to October; DS2—corresponds from November to December.

64 Air Pollution - Monitoring, Quantification and Removal of Gases and Particles

night is due to vehicles circulation related to people returning home after workday.

criteria pollutants measured at the same site. It is highlighted that with CO, NO2

peaks in population activities).

As regards the organic matter associated with the particles, only a small percentage of groups of compounds have been characterized, such as polycyclic aromatic hydrocarbons (PAHs), oxygenated polycyclic aromatic hydrocarbons (oxy-PAHs), among others [54]. The identification and quantification of PAHs, a diverse group of complex compounds with two or more fused benzene rings, has been intensively investigated [55]. In the atmosphere, PAHs can be present in particles (adsorb/absorb on their surface) or in the gas phase. It is expected that high molecular weight PAHs can be found mainly in the fine particles [56] because they have a more upper specific surface area and therefore greater adsorption than coarse particles [57].

The United States Environmental Protection Agency (USEPA) has defined 16 PAHs as a priority due to the known carcinogenic and mutagenic properties of them. It is important to note that only the European Union considers the regulation of PAHs in ambient air expressed in benzo[a] pyrene concentration (the carcinogen of reference by the IARC) at levels of 1 ng m−3 per year [58].

An essential aspect of PAHs is that they can be decomposed by photodegradation, chemical reactions with others pollutants in the urban environment, or gas-particle partition. Photodegradation is the central mechanism of chemical decomposition of 4–6 ring PAHs associated with particles. While the processes of degradation of PAHs in the gas phase involve reactions with hydroxyl radicals (•OH), ozone (O3 ), nitrate radicals (NO3 ), and reactions of basic species with acids [59]. On the other hand, in the atmosphere, the oxidation of PAHs through homogeneous and heterogeneous reactions leads to the formation of oxy- and nitro-PAHs [60, 61]. These last species are also emitted with PAHs during incomplete combustion processes [62, 63]. Oxy- and nitro-PAHs are potentially more mutagenic than PAHs, and some of these substances are furthermore potentially carcinogenic [64, 65].

Similarly, Ojeda-Castillo et al. [32] carried out a simultaneous sampling of gas phase and

thene and chrysene. The ratios of PAHs confirmed that these compounds come from the combustion of diesel. It is important to note that in previous studies, this trend is followed regardless of the particle size, which reaffirms the need to take actions to mitigate vehicle

Additionally, the health risk of lung cancer is calculated by exposure of PAHs to the respi-

More research is needed on this topic to reduce uncertainties and to specify the evaluation by vulnerable groups and exposure times. Regarding to other chemicals, the environmental

none in both phases was 1,4-phenanthrenequinone, which has been reported as a direct emission marker [74]. In the gas phase, quinones represent about 58% of the collected emissions. Inference results from sources with criteria pollutants and meteorological parameters indicate

For the GMA, the analysis of the chemical composition of particulate pollutants, especially PM2.5, has allowed us to have reliable information on some of the most common sources of emissions that affect the ambient air and probably the health of its population. The geological origin of some of the components of the particles, or an anthropogenic origin such as diesel or gasoline emissions, is one of the main sources of emission of heavy metals and toxic elements of the particles. At the same time, the most abundant species such as sulfates, nitrates, and ammonium allow us to understand that there are chemical compounds that are formed in the atmosphere and end up being incorporated into the mass of the particles. The parent gases of these secondary pollutants are frequently emitted directly from the combustion processes, highlighting the importance of this source. In the case of BC in the particles, its porous absorption nature makes them a relevant component in the issue of human health, since it is not only its abundance greater in the smallest particles, but also particulate matter reaches deeper regions of the respiratory tract, bringing numerous chemical compounds into the body, a situation that could be the origin of multiple respiratory diseases. It is probable that in some areas of the GMA one of the most critical sources of BC is the artisanal bricks industry. Thus, particular attention and monitoring must be given to the measurement of their emissions, as well as to public policies for modernization of its production processes. As for PAHs, oxy-PAHs, and quinones, the results suggest that the predominance comes from

Quinones were found in the range of 1.36–12.23 ng m−3

primary sources as the main contribution of quinones in PM1

high molecular weight quinones in the gas phase and PM1

is acceptable [72, 73]. Therefore, this result could indicate a potential risk to health.

) were found in the range of 69.34–209.77 ng m−3

the gas phase were naphthalene and acenaphthylene, while in PM1

 for the 16 priority PAHs and 8 quinones during the warm dry season of 2015 in the Centro and Tlaquepaque, being the first time that this particle size is measured in conjunction with the organic gas phase in the GMA. The levels of Σ16PAHs (sum of gas phase and

Air Pollution in an Urban Area of Mexico: Sources of Emission (Vehicular, Natural, Industrial,…

. The most abundant compounds in

http://dx.doi.org/10.5772/intechopen.80000

. It is considered that population exposure to PAHs lower

.

was benzo[b]fluoran-

67

. The most abundant qui-

, and secondary formation of

PM1

PM1

than 10−6

levels of Σ8

**4. Conclusions**

emissions in the city.

rable aerosol at a level of 1.7 × 10−3

PAHs are typical components of gasoline and diesel. They are mainly emitted into the ambient air by exhaust gases from vehicles and other sources of incomplete combustion (approximately 90% of total PAHs from these sources) [59, 66]. PAH emissions can also arise from tobacco smoke and biomass burning [67, 68]. Knowledge of the sources, chemistry, and fate of each PAHs is crucial to propose successful prevention and mitigation, thus reducing the exposure of the population [69].

A study developed in the GMA evaluated the environmental levels of 14 PAHs and 14 oxy-PAHs, associated with PM2.5 in Centro and Miravalle. Three seasons from April-2009 to January-2010 were investigated [27]. In general, the highest concentrations occurred during the cold dry season, due to the steady trend of organic pollutants to bind to particles; this behavior is directly influenced by metrological phenomena such as thermal inversions (presented 78% of the days of the year) and the stagnation of pollutants due to the orographic characteristics of the area. The most abundant compounds were benzo[a]pyrene, indene[123-*cd*]pyrene and benzo[*ghi*] perylene; the former contributing to 75% of the total carcinogenic potential and representing a public health risk in addition to the concentration of respirable PM2.5. Regarding sources of PAHs, indene[123-*cd*] pyrene and benzo[*ghi*]perylene can be considered as markers for gasoline [70]. About oxy-PAHs, the most abundant were perinaphtenone, 2-methylanthracenedione, 7*H*-benzo[*de*]anthracene-7one, and 9,10-anthraquinone. The correlations between oxy-PAHs and criteria pollutants suggest that they come from combustion processes, specifically the burning of gasoline or diesel; also, there are indications of contribution by secondary transformation sources.

Barradas-Gimate et al. [29] carried out a study in 2014 at the Centro and Tlaquepaque during the warm dry season and at Centro and Las Aguilas during the rainy season, with the objective of determining the atmospheric levels and sources of quinones in PM2.5. The highest concentration of Σ16PAHs occurred in Tlaquepaque (7.62 ± 2.03 ng m−3 ) in the warm dry season and the lowest was recorded in Las Aguilas (2.98 ± 0.55 ng m−3 ). During the rainy season, since Tlaquepaque has the characteristics of a site for the reception of pollutants concerning the Centro station, it can be inferred that there is an influence of the masses of air coming from the emission sites that promote higher concentrations of these pollutants. In this study compounds such as benzo[*ghi*] perylene, indene [*123*-cd] pyrene, benzo[*a*]pyrene and benzo[*b*] fluoranthene, 1,2-benzanthraquinone, followed by 9,10-anthraquinone and 5,12-naphtacenoquinone were the most abundant. It is suggested that some of the compounds may be associated with diesel particles and heavy vehicle activity. In general, the quinones indicate having a primary origin (vehicular). However, evidence of secondary formation was found for 9,10-phenanthrenequinone, a compound toxic for humans [71].

Similarly, Ojeda-Castillo et al. [32] carried out a simultaneous sampling of gas phase and PM1 for the 16 priority PAHs and 8 quinones during the warm dry season of 2015 in the Centro and Tlaquepaque, being the first time that this particle size is measured in conjunction with the organic gas phase in the GMA. The levels of Σ16PAHs (sum of gas phase and PM1 ) were found in the range of 69.34–209.77 ng m−3 . The most abundant compounds in the gas phase were naphthalene and acenaphthylene, while in PM1 was benzo[b]fluoranthene and chrysene. The ratios of PAHs confirmed that these compounds come from the combustion of diesel. It is important to note that in previous studies, this trend is followed regardless of the particle size, which reaffirms the need to take actions to mitigate vehicle emissions in the city.

Additionally, the health risk of lung cancer is calculated by exposure of PAHs to the respirable aerosol at a level of 1.7 × 10−3 . It is considered that population exposure to PAHs lower than 10−6 is acceptable [72, 73]. Therefore, this result could indicate a potential risk to health. More research is needed on this topic to reduce uncertainties and to specify the evaluation by vulnerable groups and exposure times. Regarding to other chemicals, the environmental levels of Σ8 Quinones were found in the range of 1.36–12.23 ng m−3 . The most abundant quinone in both phases was 1,4-phenanthrenequinone, which has been reported as a direct emission marker [74]. In the gas phase, quinones represent about 58% of the collected emissions. Inference results from sources with criteria pollutants and meteorological parameters indicate primary sources as the main contribution of quinones in PM1 , and secondary formation of high molecular weight quinones in the gas phase and PM1 .


*Article* **Power System Hardware in the Loop (PSHIL): A Holistic Testing Approach for Smart Grid Technologies**

#### **Manuel Barragán-Villarejo \*, Francisco de Paula García-López, Alejandro Marano-Marcolini and José María Maza-Ortega**

Department of Electrical Engineering, Universidad de Sevilla, Camino de los Descubrimientos s/n, 41092 Sevilla, Spain<EMAIL_ADDRESS>(F.d.P.G.-L<EMAIL_ADDRESS>(A.M.-M<EMAIL_ADDRESS>(J.M.M.-O.) **\*** Correspondence<EMAIL_ADDRESS>
Received: 20 June 2020; Accepted: 24 July 2020; Published: 28 July 2020

**Abstract:** The smart-grid era is characterized by a progressive penetration of distributed energy resources into the power systems. To ensure the safe operation of the system, it is necessary to evaluate the interactions that those devices and their associated control algorithms have between themselves and the pre-existing network. In this regard, Hardware-in-the-Loop (HIL) testing approaches are a necessary step before integrating new devices into the actual network. However, HIL is a device-oriented testing approach with some limitations, particularly considering the possible impact that the device under test may have in the power system. This paper proposes the Power System Hardware-in-the-Loop (PSHIL) concept, which widens the focus from a device- to a system-oriented testing approach. Under this perspective, it is possible to evaluate holistically the impact of a given technology over the power system, considering all of its power and control components. This paper describes in detail the PSHIL architecture and its main hardware and software components. Three application examples, using the infrastructure available in the electrical engineering laboratory of the University of Sevilla, are included, remarking the new possibilities and benefits of using PSHIL with respect to previous approaches.

**Keywords:** Hardware-in-the-Loop (HIL); Control HIL (CHIL); Power HIL (PHIL); testing of smart grid technologies

#### **1. Introduction**

The power system is undergoing a revolution nowadays, with the fast evolution of new hardware and software technologies. Power electronics, automation, cloud computing, big data, information, and communication technologies are some of the key components of the so-called smart grid. These new technologies are the enablers of the electricity sector decarbonization pursued by a society worried about energy dependence and the environmental impact of the consumed energy [1]. Without any doubt, the fulfillment of the Kyoto protocol [2] for restricting CO2 emissions and other greenhouse gases is resulting in drastic changes in terms of the generation mix of the power system, where a more active participation of renewable energies is required, but also the electrification of the transportation sector [3].

As a result, the operation and planning of power systems both in the transmission and distribution sides turn out to be even more complex than before. In fact, the uncertainty associated with renewable power sources and electrical vehicle charging, the security of supply and power quality expected by the final users with competitive energy prices and the constrained investment on network assets are stressing the power system [4]. At the same time, however, the technologies required for reducing this stress and also improving network operation are ready, but their network deployment has to be

carefully done. This is of utmost importance for those components that interact with the power system in an active manner, i.e., generators, protection devices, capacitor and reactor banks, On-Load Tap Changers (OLTCs), Flexible AC Transmission Systems (FACTS), High-Voltage DC links (HVDC), etc. Given the current automation level of the power system, where different control devices widespread along the network react to either local conditions or set points determined by a control center, it is mandatory to be sure that any new component does not interfere with the already existing ones.

For this purpose, simulation tools have brought a cost-effective solution to figure out the performance of a device or a system from its initial design stages, reducing the development costs and the time to market. The current available simulation tools cover almost all of the electrical engineering fields from electromagnetic transients to steady-state analysis. These design-support tools provide invaluable information, especially in the case of complex devices and systems. Simulation, however, is just one of the design process steps, experimental validation through a prototype or proof of concept always being required before any commercial product or even a pilot project. In fact, not all physical phenomena can be reproduced on a computer simulation tool because the reality is quite complex. In this regard, a recent branch of research, namely Hardware-in-the-Loop (HIL), focuses on the use of hardware and software tools to test devices and systems in the most realistic possible way and with a low investment. The main idea is to take advantage of the real-time simulation capabilities to test controllers or devices in a situation as close as possible to the reality. Two different and complementary options have been proposed in the HIL paradigm: Controller-Hardware-in-the-Loop (CHIL) and Power-Hardware-in-the-Loop (PHIL). Figure 1 describes these two HIL options and compares them with simulation tools. This figure highlights the simulated and physical devices (green and blue colored parts, respectively), the information flow, the power flows, and the interfaces between the actual and simulated environments. Let's consider that the objective of the simulation is to validate the performance of a device, i.e., the Device Under Test (DUT), connected to a power system. For this purpose, it is required to model all of the system components with the required detail level, but also the DUT including its control algorithms. The simulation tool can be executed on a conventional computer or a Real-Time Control System (RTCS) [5–9]. A step forward on the technology validation consists of applying CHIL [10–13]. In this case, all of the power components (power system and power components of the device) are still simulated in a RTCS, but the device control algorithm is embedded into a physical controller, e.g., a digital signal processor, with the objective of its real-time testing. The interface between the DUT (physical controller) and the RTCS is done using adequate analog/digital input/output ports as shown in Figure 1. It is possible, however, to move a step forward towards the reality by incorporating power components in the tests using a PHIL approach. This requires a power amplifier, which acts as a front end between the simulated and physical parts. The power amplifier is in charge of imposing the voltage at the point of common coupling with the DUT, which is computed by the RTCS considering its reaction. For this purpose, it is required to feedback the DUT injected current in the real-time simulated power system by using adequate analog/digital input/output ports [14–19].

In spite of the benefits provided by HIL testing, it is also important to point out that these techniques, and especially PHIL, are devoted to analyzing the behavior of a single DUT connected to a network node. Therefore, some aspects related to the introduction of a new technology in the power system can be masked. Interactions between different DUTs, the impact on the power system of a massive deployment of a new smart grid technology or the interaction between physical devices (DUTs) and the algorithms in charge of determining their setpoints are just some of these. In order to overcome these shortcomings, this paper proposes to extend the PHIL approach by: (i) introducing the algorithms determining the DUT setpoints, i.e., Algorithm Under Test (AUT); (ii) expanding the test focus from the current device-oriented approach to a system-oriented concept including the impact of the tested technology on the power system. This new approach, namely Power System Hardware-in-the-Loop (PSHIL), may allow the evaluation of the impact of a given technology over the power system in a holistic manner, considering all its power and control components.

**Figure 1.** Possible testing setups for assessing the effect of a new smart grid technology in the power system: (**a**) Simulation, (**b**) Control Hardware-in-the-Loop (CHIL), (**c**) Power-Hardware-in-the-Loop (PHIL).

The structure of this document is as follows. Section 2 elaborates on the PSHIL architecture and components. Then, the PSHIL functionalities and benefits with respect to simulation and conventional HIL approaches are outlined. Subsequently, Section 4 describes the PSHIL environment of the Department of Electrical Engineering at the University of Sevilla. Section 5 shows some case studies to evidence the wide range of possibilities that PSHIL testing may offer to the power community. Finally, the paper closes with the main conclusions and future lines of research.

#### **2. PSHIL Architecture**

This section describes the different PSHIL components and how they interact each other. Basically, a general PSHIL architecture is shown in Figure 2, where the simulated and physical environments are differentiated. On the simulation side, all the components, including network, generators, loads, and AUTs, are represented using adequate models which interact on a real-time simulation platform. In contrast, the physical side comprises a set of actual elements which may involve networks, generators, loads, DUTs, and AUTs implemented in different ways. The interaction between the simulated and the physical environments must be done through adequate interfaces. Finally, note that a management layer is required to coordinate the simultaneous testing in both environments. The next subsections are devoted to detailing each of these components.

#### *2.1. Real-Time Simulated Power System*

In this case, the power system used for testing purposes is simulated using an RTCS. Buses, branches, loads, generators, etc. are fully simulated. The physical devices (DUTs) are connected to this simulated network through a controllable voltage source, which acts as a power amplifier [20] as shown in Figure 2. The purpose of this amplifier is to physically reproduce the instantaneous voltage on a given bus of the real-time simulated network. Then, the DUT connected to the amplifier reacts to this voltage according to its control algorithm. This reaction is normally reflected in a current absorption/injection from the power amplifier, which is measured and sent back to the RTCS to be considered in the real-time simulation. In this way, it is possible also to evaluate the DUT's local impact in the simulated network. Note that this is quite convenient for testing local controllers (LCs) and

hardware components of a single DUT (PHIL). However, it is also possible to envision configurations with multiple power amplifiers for simultaneously testing different DUTs. In this case, it should be possible to evaluate the interactions between the DUTs through the real-time simulated network, but at a cost of requiring one power amplifier for each tested DUT.

**Figure 2.** Generic scheme describing the Power System Hardware-in-the-Loop (PSHIL) configuration.

#### *2.2. Physical Power System*

Two alternatives can be presented in this case: full-scaled networks and scaled-down networks. In the first case, different buses are interconnected through electrical lines and the DUTs are full-scale prototypes that interact with the different network components. Without any doubt, this testing environment gives to the test results the highest credibility because it is quite close to an actual field deployment. The space requirements and cost for high-voltage an high-power applications have to be considered, however. On the other hand, it is possible to use scaled-down networks where a change of the system base (rated voltage and rated power) is applied to scale down a given original network to an adequate value to be used on a laboratory environment. In this way, the main scaled-down magnitudes (voltage, current, and power) expressed in per-unit values are exactly the same as the ones in the original system. The buses of the scaled-down systems are interconnected through resistors and inductors with adequate values for emulating the original electrical lines. This network requires physical generators and loads that will be analyzed later. The main advantage of these physical testing environments is that some phenomena difficult to reproduce in simulations, such as heating and electromagnetic interference, always appear. The main disadvantage, however, is the reduced flexibility to modify either the topology or the parameters of the electrical network. This may hamper the impact of the DUTs on networks with different characteristics.

It is important to highlight that the PSHIL approach also considers that simulated and physical networks may coexist in a testing environment, as shown in Figure 2. This may provide an extended flexibility degree, which will be later exemplified in Section 5.1.

#### *2.3. Power Supply*

The objective of the power supply used in the physical network is to energize the testing environment providing the required power for the loads and DUTs. For this purpose, it is possible to consider the following not mutually exclusive options:

#### *2.4. Loads*

The loads are elements that absorb power from the buses of the physical power system under study. These can be classified into passive or active elements depending on the control capability:

#### *2.5. Devices and Algorithms under Test—DUTs and AUTs*

DUTs correspond to an actual device whose functionality and impact on the power system is tested using a PHIL approach. The possible list of devices that can be found in the specialized literature is endless, ranging from photovoltaic (PV) inverters [30], wind generators [31], HVDC [32], FACTS [14], energy storage systems [33], electric vehicle charging stations [26], OLTCs for power transformers [34], to digital protective relays [35]. Similarly, the functionalities that can be tested are numerous: primary control of voltage and frequency of Distributed Energy Resources (DERs) [36], current control in VSCs, inertia emulation in DERs [37,38], protection of VSCs during short-circuit faults [20], high-frequency power smoothing of renewable energy resources [39], MPPT in PV systems [40,41], etc. The simultaneous implementation of these strategies in several DUTs within a PSHIL environment allows evaluating the global impact on the power system under study, but also, mutual interactions between different DUTs can be analyzed, such as the elimination of zero-sequence current flow between transformerless grid-connected VSCs with a common DC bus [42] or the resonance frequencies originating between VSCs [43] .

The PSHIL approach, in addition, considers the possibility of also testing algorithms (AUTs) for optimizing real-time system operation and which may interact with DUTs through adequate setpoints. Therefore, the PSHIL concept extends the testing capabilities associated with the PHIL approach. Several objectives can be implemented for centralized or decentralized control structures, such as voltage control [44–46], minimization of power losses [47], congestion release [48], or the optimal allocation of ancillary services provided by different distributed control resources [49], among others. Basically, these algorithms are implemented on a computer that receives all of the network information (static and real-time data) following the general scheme outlined in Figure 2. The AUT computes and sends the DUT setpoints according to the available information and the intended objective. Furthermore, the PSHIL approach allows including in the loop the validation of other components such as the communication infrastructure, e.g., communication latencies, which are key for a comprehensive assessment of the real-time control strategies.

Therefore, the PSHIL concept simultaneously combines device- and system-oriented testing, which provides a new holistic approach especially suited to complex smart grid technologies.

#### *2.6. PSHIL Management*

The objective of the management layer is to coordinate the different components involved in the PSHIL testing platform. Basically, it consists of a hierarchical control architecture comprising two layers:

all of the required measurements. These measurements are provided to the AUTs, which may use them to compute the DUT setpoints, but are also stored for later analysis.

The information flow between the central management and LCs of generators, loads, and DUTs, as well as the interfaces between the simulated and physical networks, are key for proper PSHIL operation. Following a top-down description, the centralized PSHIL management layer is connected with each LC by using digital signals according to a communication protocol (CAN, Modbus, UDP, etc.) over a communication channel (RS-232, RS-485, Ethernet, etc.). Communication latencies depend on the performed tests, but usual values are in the order of seconds. Then, LCs are in charge of following these setpoints commanded by the centralized management layer, usually based on closed-loop algorithms to achieve a good dynamic response. For this purpose, it is required to manipulate analog signals related to the physical controlled device. This control loop latency depends on the required control bandwidth, but typical values are around dozens of microseconds. Regarding the interfaces between the simulated and physical environments, an information exchange as rapid as possible is required to avoid the problems generated by the time delays [50]. For this reason, the information exchange from the real-time simulation to the physical environment, and vice versa, is done by means of analog measurements that are regularly sampled at dozens of microseconds.

#### *2.7. Energy Management*

The energy management between the components of the PSHIL depends on whether the system under study is connected to a main electrical network or operates in islanded mode. In the first case, the electrical network is responsible for supplying or absorbing the energy necessary to maintain the power balance between loads and generators in the system. In islanded mode, however, it is required to provide adequate energy management to assure this active and reactive power balance. In this regard, two operation modes are clearly distinguished: communication-based and communication-less schemes [51]. The former schemes collect data from the PSHIL components (voltage, current, power, etc.) using a communication infrastructure in order to set the output power of the generators. Two alternative energy management algorithms within this group are usually implemented: centralized and decentralized energy management schemes [52]. The centralized algorithms receive all of the data from the PSHIL components in the Centralized PSHIL management layer and sets the operating points of the generators according to an objective, such as minimizing system operation and maintenance costs, environmental impact, or power losses [53,54]. In decentralized energy management schemes, local controllers of generation units exchange information through a communication bus. This information is used by the local controllers to determine an adequate setpoint by means of an optimization algorithm based on the shared information. Artificial-intelligence-based methods such as neural network or fuzzy systems [55] along with genetic algorithms have been used for this purpose [56]. Conversely, in communication-less schemes each generator operates independently without a communication infrastructure to exchange information with a control center or other generators. Droop-based strategies for frequency and voltage control implemented in each generation local controller are the most common techniques for energy management in this case [57].

#### **3. PSHIL Benefits**

The flexibility provided by the PSHIL approach clearly enlarges the testing capability with respect to the HIL concept. Basically, PSHIL may simultaneously test device and system functionalities provided by the DUTs and AUTs, as shown in Table 1. In this regard, it is interesting to note that the PSHIL approach is as powerful as a simulation tool but within an actual hardware environment close to the reality. Some examples of the functionalities tested using a PSHIL approach have been reported in [25,26]. In the first work, an Optimal Power Flow (OPF) for losses minimization (AUT) using an OLTC, a DC link, and distributed generators (DUTs) have been tested in a scaled-down MV distribution system. The second work deals with the integration of an electric vehicle charging station in an LV distribution network (DUT) to balance the MV/LV transformer loading (AUT). Both works

reveal that the PSHIL approach allows us to obtain specific conclusions about the DUT and AUT's performance but also the global impact of the tested technology on the network.

It has to be said, however, that this proximity to the reality is at the cost of relying on a physical network, which is difficult to reconfigure or adapt to other scenarios. In spite of this limitation, the PSHIL concept has several advantages with respect to simulation and conventional HIL strategies:

**Table 1.** Testing approaches for DUTs and AUTs.

#### **4. PSHIL Infrastructure at the University of Sevilla**

This section is devoted to describing the PSHIL infrastructure implemented in the Department of Electrical Engineering at the University of Sevilla. Figures 3 and 4 show a scheme and a view of this laboratory, respectively, whose main components are the following.

**Figure 3.** PSHIL platform of the Department of Electrical Engineering at the University of Sevilla.

**Figure 4.** PSHIL layout of the Department of Electrical Engineering at the University of Sevilla.

allows regulating the supply voltage on load. In addition, it is important to highlight that the design of this PSHIL infrastructure is quite flexible, being possible to incorporate also as DUT some of the already analyzed OLEs, or any other device. In fact, [25] analyzes the minimization of power losses (AUT) in the scaled-down distribution system using the static OLTC, the DC link, and the reactive power injections of some distributed generators emulated with the OLEs.

• PSHIL management. It has been implemented in a real-time platform provided by SpeedGoat. Particularly, this management system provides a bidirectional communication channel with the OLEs, DUTs, and AUTs by means of an UDP/IP communication protocol. The time latency with the local controllers of OLEs and DUTs is in the range of hundreds of microseconds, while the characteristic latency of AUTs depends on the algorithm but are in the range of minutes.

**Figure 5.** One-line diagram of the MV physical network within the PSHIL infrastructure at the University of Sevilla.

**Figure 6.** Elements of the PSHIL: (**a**) Regatron system, (**b**) synchronous generator, (**c**) OLEs Electrical cabinet.

#### **5. PSHIL Example of Application**

This section is devoted to describe a PSHIL application example using the University of Sevilla infrastructure presented in the above section. The objective is to evidence some of the main functionalities of a PSHIL testing environment to validate the integration of different DUTs and AUTs and their global impact on the power system under study. This application example revolves around the benefits that active distribution network management may bring to the operation of distribution systems with a high distributed energy resource (DER) penetration. This is usually studied in the MV and LV levels separately, but this section presents a simultaneous analysis of these two voltage levels. In order to do so, three different test cases following a step-by-step procedure will be presented to reach the pursued final goal.

#### *5.1. PSHIL Infrastructure Used in the Application Example*

The proposed application example comprises MV and LV distribution networks. The scaled-down distribution network supplied from the laboratory network previously described is used for this purpose. The LV network selected in this case is the residential feeder of the LV benchmark network proposed by the CIGRE Task Force C06.04.02 for the analysis of distributed generation in LV systems, shown in Figure 7 [58]. This LV network is simulated in real time using the OPAL-RT platform. It has been assumed that the LV network is connected to bus N14 of the MV scaled-down network as shown in Figure 5. The interface between these networks is done by the OLE connected at this bus following the scheme shown in Figure 8. To do so, the active and reactive powers of the LV network computed in the real-time simulation (*P*- *<sup>M</sup>*0, *<sup>Q</sup>*- *<sup>M</sup>*0) are sent to the corresponding OLE as references using the OPAL-RT analog outputs. Conversely, the voltage measured in bus N14 of the MV scaled-down network is sent to the real-time simulation to be considered as the bus M0 voltage using the OPAL-RT analog inputs. Note that the OLE connected to bus N14 also has to emulate an industrial load with power references (*P*- <sup>14</sup>*ind*, *<sup>Q</sup>*- <sup>14</sup>*ind*) sent from the management layer. Therefore, the OLE reference powers (*P*- *<sup>N</sup>*14, *<sup>Q</sup>*- *<sup>N</sup>*14) are the sum of these two setpoints.

**Figure 7.** PSHIL application example. One-line diagram of the LV real-time simulated network.

**Figure 8.** PSHIL application example. Integration of the LV real-time simulated network and the physical MV network.

Regarding the MV scaled-down physical network, the OLEs are connected to the following buses: N3, N5, N6, N7, N8, N9, and N10. Each bus has a combination of generators (photovoltaic and wind turbine) and loads (industrial and domestic types) with a peak power defined in Table 2. These peak power values correspond to those of the scaled-down system being required to apply the corresponding scale factor to obtain the values referred to the original system (20 MVA/100 kVA). A constant power factor of 0.87 for industrial loads and 0.98 for domestic loads has been assumed. The 24-h active power profiles of loads and generators are detailed in Figure 9 in per-unit values. The DUTs to be tested connected to this physical system are the distributed generation emulated by OLEs, the OLTC, and the DC link. These DUTs follow the optimal setpoints computed by an OPF that minimizes the power losses (AUT) [25].

**Table 2.** Peak power of loads and generators connected in each node of the MV scaled-down distribution network.

**Figure 9.** PSHIL application example. Daily profiles in per-unit (**a**) Load profiles, (**b**) Generation profiles.

Similarly, the loads and generators connected to the LV real-time simulated network get the same power profiles than the ones used in the physical system and represented in Figure 9. The peak power of the load and generators connected to each node is summarized in Table 3. Note that these values correspond to the original system rather than a scaled-down one because this network is simulated. Therefore, the corresponding scale factor must be applied to compute the reference powers (*P*- 14, *Q*- <sup>14</sup>). The AUT to be tested considers the reduced communication infrastructure associated with LV distribution systems, which prevents the use of secondary controllers like the one applied in the MV network. In this case, it is proposed to test an AUT that assigns a voltage reference to each distributed generator of the network, computed on a planning stage based on historical data. The local controllers of the distributed generators apply a reactive power droop strategy to achieve the assigned voltage reference.

#### *5.2. PSHIL Test Cases*

The test cases used to demonstrate the benefits of an active management of MV and LV distribution systems have been organized in a step-by-step manner as follows:

**Table 3.** Peak power of loads and generators connected in each node of the LV original distribution network.

Table 4 collects the DUTs/AUTs and the functionalities tested in each case. This table clearly shows the step-by-step procedure followed to reach the final goal of simultaneous testing of an active management of MV and LV distribution systems with a high DER penetration.

**Table 4.** DUT/AUT and functionalities of the test cases.

#### *5.3. PSHIL Experimental Results*

#### 5.3.1. PSHIL-1 Results

Figure 10a shows the 24-h active and reactive power profiles of the OLE connected to bus N14 for the test case PSHIL-1. This OLE is in charge of emulating an industrial load, detailed in Table 2, and also the integration of the LV real-time simulated network into the physical MV scaled-down system. The continuous lines represent the actual OLE active and reactive powers, while the dashed lines correspond to the setpoints (sum of the industrial load and LV real-time simulated network). It can be observed that there is hardly any difference between the power references and the actual injected values. In addition, Figure 10b compares the voltages measured at bus N14 and the ones used in the real-time simulation for bus M0. Both voltages are practically coincident, similarly to what

happens with the powers. Therefore, these results confirm an adequate integration of the LV real-time simulated network into the physical MV scaled-down network.

**Figure 10.** PSHIL-1: N14 industrial load and integration of the LV real-time simulated network within the MV scaled-down physical system. (**a**) 24-h evolution of active and reactive power injected by the OLE connected to bus N14. (**b**) 24-h evolution of voltages in bus N14 (physical network) and M0 (real-time simulated network).

Figure 11a shows the daily voltages of buses N3, N6, and N14 where undervoltage situations (voltages below 0.95 pu) during some periods are noticed. These undervoltages are more severe in those buses located farther away from the primary substation and mainly concentrated during the hours without DER injections. Conversely, these are responsible for the voltage peaks around 11:00 in nodes N3 and N14 because of the DER-related active power profile shown in Figure 9. It is interesting to note that this effect is not reproduced in bus N14 because the DC link is not connected in this test case and the net active power of the industrial load and the real-time simulated LV network is always negative (consumption), as shown in Figure 10a.

Figure 11b illustrates the nodal voltages of buses M1, M4, and M7 within the real-time simulated LV system. Similarly to the MV system, undervoltages arise around 20:00 and peak voltages around 11:00 when PV generation is peaking. Note that this effect is more prominent in bus M7, where PV generation is connected, becoming even higher than voltages in buses M4 and M1, which indicates a reverse power flow in the lines connecting these nodes. Bus M1 voltage is barely modified during this period of PV peaking because of its proximity to the MV/LV transformer. It can be observed, anyway, that the general voltage trend in the LV network is imposed by bus N14 of the MV network, especially in those periods without LV DER injections. These conclusions arise from the simultaneous analysis of the MV and LV networks facilitated by the proposed PSHIL approach.

**Figure 11.** PSHIL-1. Impact on the MV and LV distribution networks. (**a**) 24-h evolution of the MV network voltages at buses N3, N6, and N14. (**b**) 24-h evolution of the LV network voltages at buses M1, M4, and M7.

#### 5.3.2. PSHIL-2 Results

The AUT in the test case PSHIL-2 is an OPF for minimization of power losses in charge of defining the setpoints of the MV DUTs (OLTC, DERs, and DC link), whose daily evolution is detailed in Figure 12. The following comments can be stated for each of these DUTs:

Therefore, it can be claimed that the adequate control actions are computed by the AUT and executed correspondingly by the corresponding DUTs.

**Figure 12.** PSHIL-2. DUT performance using 24-h evolution of: (**a**) OLTC tap position and voltage of bus N3. (**b**) DER reactive power injections in buses N3 and N6. (**c**) DC-link reactive power injections. (**d**) DC-link active power transfer from N8 to N14.

Regarding the impact of the AUT on the tested power system, it is interesting to note the evolution of the MV and LV voltages shown in Figure 13. This figure represents the voltages of the same nodes represented in Figure 11 for the test PSHIL-1. Regarding the MV nodes, it can be clearly noticed that most of the time the voltages are above 1 pu and very close to 1.05 pu, which is the maximum voltage considered in the OPF. In addition, a flatter voltage profile can be observed compared to those of Figure 11a in spite of the highly variable DER generation. On the other hand, the evolution of the LV buses detailed in Figure 13b shows a similar evolution than the voltage of bus N14. This is reflected in a voltage increase of all LV buses thanks to the AUT and DUTs of the MV network, which avoid undervoltage situations (below 0.95 pu). This effect is especially remarked around hour 11, where the OLTC action drastically reduces the voltages of the LV buses. Finally, it is interesting to note the AUT effect on the power losses of the MV and LV networks, which have been reduced with respect to PSHIL-1 from 0.584% to 0.465% and 7.086% to 6.382%, respectively.

**Figure 13.** PSHIL-2. Impact on the MV and LV distribution networks. (**a**) 24-h evolution of the MV network voltages at buses N3, N6, and N14. (**b**) 24-h evolution of the LV network voltages at buses M1, M4, and M7.

#### 5.3.3. PSHIL-3 Results

In this case, the same voltage reference was imposed for all of the LV PV generators, for simplification purposes. Figure 14 shows the voltage and the active and reactive powers injected in by the PV connected in node M7. The bus voltage is compared with the reference voltage (1.05 pu) to compute the reactive power injection using a linear droop strategy. Note that the reactive power injection shown in Figure 14b is always positive because the nodal voltage is most of the time below the reference voltage, as shown in Figure 14a. This is especially noticeable at night when the voltages are low due to the network loading and the PV uses most of its capacity to inject reactive power according to the rated power detailed in Table 3.

**Figure 14.** PSHIL-3. (**a**) 24-h evolution of voltage in bus M7. (**b**) 24-h evolution of PV active and reactive power injections in bus M7.

Regarding the impact of these control actions on the distribution system, Figure 15 shows the daily voltage profiles on the MV and LV networks. The MV voltages, shown in Figure 14a, are almost the same as those presented in PSHIL-2 because only one LV distribution network has been simulated and, therefore, its effect on the MV side is barely noticed. The analysis of the LV voltages, however, reveal some differences due to the voltage control algorithm implemented in this network. During the midday hours, the PV power injection causes the voltage in bus M7 to be higher than the voltage in bus M1, revealing an inverse power flow between these two LV nodes. During the evening, with the peak load and the absence of PV active power injection, the voltage decreases. However, the influence of the reactive power injection using the spare PV converter capacity is noticeable comparing the voltage of buses M4 and M7. Note that in this evening period, the voltage at bus M7 is very close to that of bus M4, even though its electrical distance to the head node is larger. This effect is also noticeable when comparing these nodal voltages in the test PSHIL-2 (Figure 13b), where no LV control actions are applied. Finally and regarding the power losses, the LV local voltage control achieves a reduction of power losses from 6.382% reported in PSHIL-2 to 4.590%.

**Figure 15.** PSHIL-3. Impact on the MV and LV distribution networks. (**a**) 24-h evolution of the MV system voltage in buses N3, N6, and N14. (**b**) 24-h evolution of the LV system voltage in buses M1, M4, and M7.

#### **6. Conclusions**

This paper has presented a new HIL approach for simultaneously testing several DUTs and AUTs interacting with each other in a power system. In this way, the proposed PSHIL approach can be considered a natural evolution of the PHIL concept, which is mainly focused on the individual performance of a DUT connected to a network node. The PSHIL architecture has been depicted including a description of its main actual and simulated components classified according to their functionalities.

The PHSIL infrastructure available at the electrical engineering laboratory of the University of Sevilla has been used as an example of application to evidence the PSHIL testing functionalities, analyzing the active management of a MV and LV distribution system with high penetration of renewable generation. Different cases have been tested, each of which evidences some of the possible functionalities of the PSHIL approach.

The first test (PSHIL-1) shows how to integrate an LV real-time simulated network into a physical scaled-down MV system. The second one (PSHIL-2), using the first test as a starting point, elaborates on the use of an OPF (AUT) for power losses minimization in the physical MV scaled-down network combined with several DUTs (OLTC, DER reactive power, and a DC link) in charge of following the OPF-computed optimal setpoints. Finally, the third test case (PSHIL-3) introduces a voltage control algorithm in the LV network based on local conditions in buses where DERs are connected.

In conclusion, the staged testing procedure has demonstrated the PSHIL capacity of simultaneously analyzing an MV and LV distribution system in a hardware environment. Moreover, the results shown in the previous section evidence that the individual performance of each DUT can be successfully studied, as well as their impact on the power system. The PSHIL concept allows an evolution from a deviceto a system-oriented testing approach, providing a powerful framework for the study of smart grid technologies and allowing an assessment of the potential benefits that new technologies may bring to the power system.

**Author Contributions:** Conceptualization, M.B.-V. and J.M.M.-O.; Methodology, M.B.-V.; Software, ; F.d.P.G.-L. and A.M.-M.; Validation, M.B.-V. and F.d.P.G.-L.; Formal Analysis, J.M.M.-O. and A.M.-M.; Investigation, M.B.-V.; Resources, J.M.M.-O.; Data Curation, F.d.P.G.-L.; Writing—Original Draft Preparation, M.B.-V., J.M.M.-O. and

A.M.-M.; Writing—Review & Editing, M.B.-V., J.M.M.-O. and A.M.-M.; Visualization, F.d.P.G.-L.; Supervision, M.B.-V. and J.M.M.-O.; Project Administration, J.M.M.-O.; Funding Acquisition, J.M.M.-O. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Spanish Ministry of Economy and Competitiveness and the European Union Horizon 2020 under grant numbers ENE2017-84813-R and 764090, respectively. The APC was funded by the Ministry of Economy and Competitiveness under grant number ENE2017-84813-R.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **Abbreviations**

The following abbreviations are used in this manuscript:

#### **References**

and Delivery of Electrical Energy in the 21st Century, Pittsburgh, PA, USA, 20–24 July 2008; pp. 1–6. doi:10.1109/PES.2008.4596142. [CrossRef]

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).


**4. Kinematic viscosity induced effect on pressure, kinetic bio-energy and potential bio-energy**

One known effect of an increase in viscous forces is the ability to conduct negative work (drop in fluid pressure along the flow path) on the fluid, reducing its macroscopic mechanical energy while increasing the internal energy (microscopic kinetic/molecular ionic potential energy) and resulting in a slight increase in temperature [4]. Since pressure is a measure of fluid mechanical energy per unit *The Influence of a Diamagnetic Copper Induced Field on Ion Flow and the Bernoulli Effect… DOI: http://dx.doi.org/10.5772/intechopen.99175*

volume, the correlation of a decrease in macroscopic mechanical pressure along the flow path (i.e., through vessels or across membranes) along with the increased internal energy (kinetic and potential bio-energy) of the fluid is noted in the Bernoulli Equation below (Eq. (3)) [25]:

$$\mathbf{P\_{\tiny{u}}} + \mathsf{Val}\,\mathsf{p}\mathbf{v\_{\tiny{u}}}^{\ast} + \mathsf{p}\mathbf{g}\mathbf{h\_{\tiny{u}}} = \mathbf{P\_{\tiny{u}}} + \mathsf{Val}\,\mathsf{p}\mathbf{v\_{\tiny{u}}}^{\ast} + \mathsf{p}\mathbf{g}\mathbf{h\_{\tiny{u}}} \tag{3}$$

The variables **P1, ν1** and **h1** refer to the pressure, speed and height of the fluid at the initiation point and the variables **P**2**, ν2 2** and **h2** refer to the pressure, speed and height of the fluid as it flows to another point. Also, **½ ρν2** = kinetic energy per unit volume (Eq. (4)) and **ρgh** = potential energy per unit volume [25]. It is known that P2 < P1 for as the fluid transitions along the flow path, the pressure energy decreases while the kinetic energy and potential energy increase. In other words, increased fluid speed creates decreased internal pressure. This Bernoulli Equation as well as viscosity induced by a DEP EMF may indeed correlate to a conservation of energy principle where the increase in viscosity may trigger the lowering of pressure in the regions where the flow/velocity is increased along with increased kinetic bio-energy and potential bio-energy.

The average kinetic energy (**KE avg**) per unit volume (**V**) of flowing fluid can be expressed in terms of the fluid density (**ρ**) and maximum flow velocity (**νm**) (Eq. (4)) [25]:

$$\mathbf{KE}\_{\text{avg}} \;/\; \mathbf{V} = \left(\mathsf{V}\mathsf{z}\mathsf{p}\right)\left(\mathsf{v}^{\mathsf{z}}\mathsf{z} \;/\; \mathsf{3}\right) \tag{4}$$

When the kinetic energy of fluid is examined with regards to laminar flow (as occurs in the plasma and across the cellular membranes) one must take the average of the velocity (shear stress and velocity gradient = viscosity) squared into account. The relationship between velocity and viscosity again speaks to the strong correlation viscosity has to kinetic energy. Increased viscosity and increased velocity may indeed be a significant factor in the internal kinetic energy (and pressure changes as well) and temperature regulation in biological systems. Might this non-uniform 2.5 ampere DEP EMF driven increase in kinematic viscosity offer a Bernoulli effect/ conservation of energy (via increased kinematic viscosity➔ increased averaged velocity ➔ decreased pressure) in biological systems? Could this also have implications for membrane flow, plasma flow, blood pressure regulation and temperature control in the living organisms [9, 12, 23]?

One area where potential energy of a moving fluid in biological systems may reside would be in or near the membranes of cells. According to Dr. Pollack and EZ water (fourth phase) can be seen to function as a battery [22]. There have also been additional studies that suggest this structured or EZ water may increase the magnetic property or diamagnetism of the chloride ion and significantly modulate chloride ion channels both across the membranes and on the surface of red blood cells [9, 11–13, 23]. As stated earlier, chloride is a diamagnetic ion (that displays dielectric properties) that is both repelled and driven by the magnetic field. This dielectric behavior may offer a diamagnetic anisotropy mechanism in the membrane (**Figure 6**). *In vitro* and human studies using a copper influenced DEP EMF have shown a significant increase in chloride channel modulation [9, 11–13, 23]. The paramagnetic cations (Na+ , Mg+ , Ca+, K+ , H+ ) move in the direction of the field or with the flow of the field, while the diamagnetic chloride anion will move in opposition to the field, acting as a field separator and facilitator of movement (through repulsion) for the cations (**Figure 6**). Chloride ion channel inhibition has been

#### **Figure 6.**

*The internal potential molecular ionic attraction energy forms an exclusion zone or the fourth phase of water. The internal potential energy driven molecular ionic attraction facilitates a "like attracts like" or "like likes like" (EZ water) where chloride and water create crystalline structure with water tetrahedrons as they absorb magnetic energy [9, 14, 22] (***Figures 2***–***4***). The hydrogen ions bond or coalesce (molecular ionic attraction potential energy) and the negatively charged diamagnetic chloride anion (with an oxygen operating as a charge separator- bio-chloride/EZ water) spins in opposition to the field facilitating the passage (kinetic energy) of the charged cations through the field/membranes. This constitutes diamagnetic anisotropy and basically catapults the cations through the membrane with electromagnetically driven kinetic energy and potential energy in biological systems (***Figure 5***) [9, 14, 22].*

noted with increased levels of extracellular ATP [26]. Volume sensitive chloride channels have also been found to be regulated by intracellular ATP concentrations [27]. Since the application of this DEP EMF has shown significant upregulation of chloride ion channels in *in vitro* studies, might this external DEP EMF application offer an external energy source that is not dependent or regulated by ATP levels in the organism? Could this offer an energy conservation source for cellular function within biological systems?