**1. Introduction**

There has been an increased interest in the use of renewable energy sources, which is due to the limitations in fossil fuel reserves and to planet pollution. The research in the new sources of energy, such as photovoltaic, wind and fuel cells can be used to enhance the safety, reliability and sustainability of the planet, because they are environmentally friendly, highly efficient and renewable cycles. Particularly fuel cells appear in this context as an attractive power source because they generate electricity from hydrogen through an electrochemical process, which is virtually free of emissions and noise and only water and heat are the by-products. In addition, they present several advantages such as: silent, high potential for cogeneration applications, adaptable to a wide range of power and applications.

In this context, the chapter main goal is the analysis design and implementation of power generation systems based on fuel cells, which demands to careful selection of both; i) the fuel cell model and ii) the power electronic converter.

Then, the first part of the chapter presents and characterizes a semi-empirical model of the PEM fuel cell, including its static and dynamic behaviours and thorough some experimental tests made with the commercial system Mark 1020 constraints imposed by the PEM to the converter are established, regardless the topology of the power converter.

The second part of the chapter make considerations on the most suitable topologies of converter for this application type, and considering the requirements imposed by the PEM Mark 1020, a new efficient high power converter topology is selected, designed and imple‐ mented experimentally.

© 2013 Outeiro and Carvalho; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2013 Outeiro and Carvalho; licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

The converter follows a resonant approach that provides low component stresses, high frequency operation, soft-switching commutation, and operation under a wide range of input and output conditions.

The control of the converter is divided into two parts, namely: i) the voltage controller, which is responsible for keeping constant the output voltage of the converter under loading variations and ii) the PEM controller, which is responsible for improving the performance by keeping the PEM fuel cell in its optimal operating point.

The results are firstly presented for the PEM fuel cell and then for the whole system with load disturbance.

The results demonstrate that the proposed converter is a good selection to improve the efficiency of PEM fuel cells because it allows an adequate control of the power delivered by the fuel cell while maintaining the requirements imposed by the load and minimizing the losses by using soft-switching control.


**3. Results**

Given the growing importance of sensor city solutions in the world, technology giant companies (e.g., Huawei, Ericsson, Microsoft, Oracle, Alibaba, Deutsche Telekom, Samsung) are involved in numerous urban technological projects in order to provide platforms, products and services in line with the necessary paradigm shift [111]. Thus, sensor cities aim to involve citizens and companies in the process of planning, monitoring, and analyzing of urban processes and raising awareness and comprehension on environmental, social, and economic issues [112,113]. Table A1 in the Appendix A presents some examples of cities equipped with real-time monitoring stations and sensors that analyze and collect urban performance.

Few [114] in his book 'Information Dashboard Design: The E ffective Visual Communication of Data' defines the dashboard as a visual, consolidated and organized display on a single screen of the most important information needed to monitor, analyze and achieve one or more urban objectives. Similarly, Kitchin [115] explains urban dashboards as digital, physical or mixed interfaces that allow users to actively or passively interact in urban data monitoring and management in order to improve understanding of urban systems. Kitchin and McArdle [116] and Petit and Leoa [117] describe urban dashboards as platforms that use dynamic and interactive graphic interfaces, maps, 3D models, augmented reality, bar charts, and so on, to support urban decision-making with the aim of monitoring, analyzing and interpreting performance and trends of cities. Thus, the sensor city is generally accepted as a digital platform where users are completely immersed.

Nevertheless, theoretically, the ideals that characterize sensor cities can be defined as universal and homogeneous, while contexts tend to be specific and heterogeneous [118]. For example, cities have di fferent social, economic, environmental and technological infrastructures and are governed by di fferent political and bureaucratic systems with interests that are di fficult to combine in a single urban project. Furthermore, while urban data is increasingly easy to access, technical and digital skills are di fficult to find in some urban contexts around the world, in order to manage, analyze and interpret urban data.

Urban contexts using IoT and/or ICT platforms require more initial e fforts due to the assembly and maintenance of the infrastructure, in order to adapt it to increasingly complex urban scenarios [119,120]. In detail, it is a matter of integrating devices that are: (a) Heterogeneous, capable of generating di fferent types of data; (b) Powered by di fferent energy sources, such as battery or renewable energy; (c) Dynamic and flexible, capable of analyzing constantly changing urban scenarios, and; (d) Unpredictable, in the sense that technological applications can provide conflicting results by analyzing the same data, as they use di fferent protocols and standards.

The significant costs of designing, installing, and maintaining monitor stations and sensor technologies represent a barrier to entry for smaller cities or those located in less development regions [121,122]. The success of an urban sensor strategy depends essentially on the economic, social and environmental characteristics of the urban context taken into consideration together with organizational, ethical and transdisciplinary factors of the actors involved [118].

The sensor city framework is illustrated in Figure 2. In detail, the operational phases, integrated in a holistic perspective of the urban context, are divided into planning, sensing, collecting, processing and analysis of urban data and results.

In Phase 1, or planning, the role of ICTs is highlighted, which allows citizens to participate in the decision-making process and to enhance systemic collaboration between stakeholders involved, contributing significantly to greater comprehension, transparency and accountability [123–125]. Hence, planning activities through smart solutions permits a holistic and integrated approach of the various urban dimensions (e.g., governance, economy, environment, mobility, living, and people), reducing costs and time of the bureaucratic collaborations between departments and improving quality and e fficiency of urban services [126]. Nonetheless, most cities do not work like companies (e.g., IBM, Cisco, Google). They tend to be disorganized, e.g., departments do not collaborate on solutions [127]. On this point, Cugurullo [128] elaborates that urban contexts promoted as examples of integrated and holistic urban planning are often fragmented and disconnected, characterized by several incompatible components.

**Figure 2.** Sensor city framework (source: authors).

The Digital Single Market strategy adopted by the European Commission (EC) represents one of the fundamental pillars of the policies for creating a digital single environment [129]. Indeed, the strategy aims to ensure a better, secure and uniform access for citizens to digital networks in order to improve technological knowledge and encourage greater social inclusion.

The governance dimension highlights the spread of social media (e.g., Facebook, Twitter, Instagram) and other data sharing platforms that allow increasingly integrated and structured communications between stakeholders [130]. Thus, by adopting a bottom-up approach, these feedback-generation, sharing and management tools transform citizens, companies, local authorities, and so on, into active participants in the governance of sensor cities [131–133].

The environmental dimension involves various aspects of urban context such as:

The mobility dimension includes, amongst others, smart and sustainable public transport system, availability of urban infrastructure suitable for autonomous vehicles, car sharing stations for electric vehicles and ICTs relating to tra ffic and road monitoring [25,136–138].

The aspects related to the living dimension include actions to improve the use of cultural and entertainment facilities (e.g., libraries, museums, schools, public parks, sport facilities) [20].

The people dimension includes policies aimed at promoting a 'sustainable' community, improving digital literacy, providing various assistance programs for citizens with special needs and a quality healthcare system, ensuring gender equality in terms of pay and o ffice positions, safe and healthy work environments, and so on [139].

The economic dimension refers to the ability of the urban context to favor a path of growth through technological innovation, entrepreneurship and sustainability, attracting the most innovative companies, start-ups and talents, and capable of promoting a digitalized and collaborative development [32].

In Phase 2, with the objective of sensing, collecting, processing and analyzing urban data, the Chinese telecom company Huawei, for example, has developed 'Smart City Solution', a platform used in over 160 cities in 40 countries in Asia and Europe, capable of analyzing large volumes of real-time urban data and providing policymakers a method of predictive analysis of future urban scenarios (Figure 3). Specifically, Lanzhou New Area represents the first new state-level development area in northwest China which, through the support of Huawei's network, was able to build the nation's first governmental IoT and wireless sensors network that integrates both broadband and narrowband communications, integrating 31 departments (e.g., public safety, finance, energy, transport, healthcare, education) with 45 eLTE stations. The program aims to improve the quality of life by optimizing urban resources in a smart and sustainable manner [140].

**Figure 3.** Huawei Digital Platform, derived from [141].

Similarly, Cisco has developed the 'Cisco Kinetic for Cities' framework, an open and easy-to-use urban data sharing platform that combines data provided by sensors, applications and other third-party devices to create a dynamic sensor city infrastructure, encouraging the exchange of innovative urban initiatives between policymakers, companies and citizens (Figure 4) [142].

**Figure 4.** The Cisco Smart + Connected Digital Platform, derived from [143].

At the same time, Alibaba has developed the 'City Brain' program that has also become a useful tool for city managers, providing a holistic dashboard that can improve the perception of urban data and real-time processing capacity [144]. Figure 5 shows the real-time detection and analysis platform for city events. By integrating the data from the surveillance rooms, it is possible to coordinate traffic lights to give priority passage to response vehicles (e.g., police, fire-fighters, rescue and other vehicles) in case of emergency. Likewise, the program has been launched in several Chinese cities such as Hangzhou, Shanghai, Chongqing, Suzhou, Haikou, Beijing, Chengdu, Quzhou, and Jiaxing. Projects as CityBrain highlight the continuous interaction between local authorities and technology companies in managing urban governance. In fact, the Chinese platforms are largely owned by national technology companies [60].

**Figure 5.** Alibaba City Event detection and smart processing, derived from [145].

Barcelona has changed its sensor city approach from top-down to bottom-up, involving its citizens in participating in innovative urban projects. For example, the Smart Citizen Kit is a dashboard that collects data on the environmental such as air composition, temperature, light intensity, sound levels and humidity through sensors and ICTs [146]. The data collected in real-time are sent via Wi-Fi to an open data platform and are used to create maps that display environmental conditions, equipping public and private stakeholders with urban data in order to develop and/or improve services for citizens [147]. Telefonica—through the Valencia Smart City Project—aims to transform the city of Valencia into an intelligent ecosystem and fully connected via 350 sensors, allowing the management of public resources through a single ICT platform and improving several urban areas such as transport, energy, e fficiency and environmental services. The city of Santander has installed around 20,000 parking sensors in the streets, introducing intelligent waste containers capable of monitoring and measuring air pollution, rainfall, and tra ffic density. Through the integration and interpretation of the corresponding data, the Santander municipal administration optimizes waste truck routes to save sta ff and fuel costs, or controls the irrigation of city parks to save water [148].

Deutsche Telekom have implemented similar dashboard, equipping urban physical object (e.g., street lamps, bins, parking lots, tra ffic lights) with software, sensors and connectivity systems integrated into a shared network. In this sense, the collection, monitoring, analysis and interpretation of urban data allows the implementation of innovative service and business models (Figure 6).

**Figure 6.** Smart City Dashboard developed by Deutsche Telekom for Hungary subsidiary, derived from [149].

In 2018, Deutsche Telekom and the city of Hamburg implemented around 11,000 parking spaces equipped with sensors that can provide the current availability status to users via the app [150]. The local authorities of Gelsenkirchen (Germany) have decided to collaborate with Huawei and GELSEN-NET in order to implement a new open and shared urban governance system, developing an ICT infrastructure capable of integrating key data to improve the e fficiency of the public services and the accuracy of the decision-making process. In particular, the 'Safe City' program uses Huawei's extensive wired and wireless broadband network and the IoT that connects industrial parks, hospitals, schools, pedestrian areas and urban centers, creating a sustainable ICT ecosystem. As part of the T-City project in Friedrichshafen (Germany), Deutsche Telekom has partnered with Alcatel-Lucent to provide hardware solutions and network devices for T-City. Specifically, Deutsche Telekom has developed over 40 pilot projects in several categories such as mobility, research, tourism, culture, health, and employment.

The LuxTurrim5G ecosystem in Espoo (Finland) aims to transform the Kera area into a sustainable and digitized urban neighborhood, involving Nokia and several partners in order to create a multitude of urban digital services. The LuxTurrim5G platform is able to collect, store, manage and share large amounts of urban data and solutions in a safe and efficient way between local authorities, companies and citizens. Specifically, the network developed by Nokia includes, among other things, over 50 Wi-Fi devices, 75 cameras, 49 different sensors that monitor air quality, climate, temperature, road surface conditions as well as CO2 levels, 9 radar devices, 7 information screens, a charging station for electric vehicles and a charging and landing station for drones.

Oracle through a partnership with the Indian state of Maharashtra has implemented a platform to design, develop and analyze government-to-citizen and smart-business government services across the state. Maharashtra also aims to connect all its 113 million residents via fiber, including those in the more than 300 cities of Maharashtra and its 29,000 villages. Dallas has chosen to collaborate with Ericsson to implement an advanced traffic management system through a dashboard capable of monitoring, managing, analyzing and aggregating different data in real-time from traffic sensors and cameras to dynamically control the traffic lights. The goal of the city is to have an intuitive and shared analysis tool capable of integrating data from the various departments and agencies.

According to the report 'The future of street lighting', street lighting represents the main part (about 40%) of a city's overall electricity costs. In this sense, the replacement of conventional lamps with LED bulbs can reduce energy consumption by up to 80% when using a centralized management system. For example, intelligent lighting platform of Cisco and Sensity in Kansas transforms each lamppost into a sensor connected to a broadband wireless network, creating a true interconnected lighting network capable of collecting real-time data such as intelligent parking systems, electricity consumption, or air quality level. In this sense, the smart pole developed by Deutsche Telekom and Nokia represents a paradigm shift in the use of lighting poles as it goes beyond simple urban lighting and constitutes an essential component of the infrastructure of sensor cities (Figure 7).

The Hamburg Port Authority (HPA) through Cisco systems has developed sensors that monitor the use of resources (e.g., trucks, cranes, means of transport, ships) and infrastructures (e.g., roads, parking lots, storage warehouses) in one of the busiest and most important ports in Europe. In this sense, modernization through IoT technological innovation allows the Port Road Management Center to plan future investments in the traffic infrastructure, optimizing the flow of traffic and minimizing the externalities of the port on the inhabitants of the city.

Ericsson aims to make Stockholm the smartest city in the world by 2040. With the water Monitoring Network project, Ericsson has the ability to design and implement a real-time water quality-monitoring network using an IoT sensor system located in the Stockholm water system. In addition, big data analytics are used that can analyze the data produced by the sensors and provide more information on changes in water quality such as pH and temperature.

In many cases, small towns do not need sophisticated public transportation solutions. In this sense, the challenge is to combine the departure time of buses, subways, railways, and so on, with data from traffic jams and road works on highways, showing the best alternative connections. As a partner of the Kooperation Östliches Ruhrgebiet, Deutsche Telekom helps to connect local public transport in the German state of North Rhine-Westphalia. In this regard, Deutsche Telekom's strategy allows not only meeting the needs of passengers in terms of both transparency in information (e.g., timetables, delays) and efficiency, sharing the same platform for multiple tasks. In Croatia, Deutsche Telekom's subsidiary, Hrvatski Telekom, has developed an electric vehicle charging network of 145 charging points in 101 charging stations in 70 cities. The project also integrates an ICT infrastructure that helps users to book and pay for vehicles top-ups and receive real-time availability information.

The final phase concerning the results explaining the decisive role of sensor solutions in terms of implementation of innovative urban processes (e.g., efficient use of natural resources, cross departmental integration, real-monitoring of traffic congestion and energy consumption, smart management of infrastructures), in order to strategically improve the contribution of sensor cities to disruptive urban development. Consequently, the optimization of Quality of Experience (QoE) and Quality of Services (QoS) has become a crucial aspect in the implementation of urban services and processes [153]. Particularly, QoE represents an evaluation of human experience when interacting with technology solutions and stakeholders in a given context. Therefore, a detailed analysis of the QoE should consider several actors interacting with each other at different levels (environmental, social, economic, technological) and with different goals. To do this, it is necessary to define the main interactions between citizens, companies, local authorities, social organizations, and so on. In sum, some QoE aspects related to sensor city are listed in Table 1.

**Table 1.** Impact of quality of experience on sensor city, derived from [153].

In this regard, the challenge for policymakers will be the widespread use of several tools, such as devices, platforms, algorithms, and networks, to access, share and integrate the largest number and type of urban data. The big data evolution process requires significant data gathering, highly specialized personnel, and infrastructures owned by different municipalities, agencies, corporations, and private companies [5]. This attention of ICT leads urbanist mark Swilling to describe the sensor city as a form of algorithmic urbanism [154]. In this sense, concepts, such as big data analysis, ICT, sensors, real-time monitoring systems, smart infrastructures, IoT, represent necessary but not sufficient solutions [105,115,155]. The reconfiguration of the urban system expresses the need to integrate the environmental, social, economic dimensions of smart technologies into an integrated, efficient, and computerized urban context [6,49,156,157].


**7.1 Monitoring of hemodynamic and vital constants**

Several systems enable the delivery of mobile health monitoring services for outpatients and/or those outside of healthcare settings. Examples of these systems include the pulse oximeter of Masimo.com, the adhesive patch of Isansys.com, the electronic tattoo of mc10inc.com, or necklace of Tosense.com.

Several parameters can be analyzed such as: heart rate, heart rate variability, respiratory rate, skin temperature, stroke volume and cardiac output.

The information is transmitted to the Smartphone so that health professionals and/or patients can be informed quickly in case of clinical deterioration [61].

## **7.2 Remote monitoring of surgical wounds**

Postoperatively, the Smartphone can be used as a tool for the monitoring and surveillance of postoperative infection sites. These infections can occur up to 30 days postoperatively. Monitoring can be done through sharing images of the surgical site and remote video conferencing [62, 63].


*3.2.10. Three-dimensional knitted spacer or sandwiched structure*

The 3D knitted spacer fabric consists of two separate fabric layers (top and bottom surfaces) that are connected by intermediary yarns or knitted layers [79]. The top and bottom fabrics can be weft or warp knitted fabrics with or without inlays. Three-dimensional spacer fabrics are renowned for their excellent resilience and air permeability properties. Figure 21 shows schematic and real views of various 3D knitted sandwich fabrics.

## *3.2.11. Three-dimensional nonwoven fabric*

Poisson's ratios throughout its structure [77]. Another multiaxial 3D braided structure has ±bias yarns placed in-plane, and warp (axial), radial (z-yarns), and ±braider yarns placed outof-plane [78]. The braider yarns are intertwined with the axial yarns whereas ±bias yarns are oriented at the surface of the structure and locked by the radial yarns to the other yarn sets. Figure 19 shows the multiaxial cylindrical and conical para-aramid 3D braided structures. The properties of the multiaxial 3D braided structure in the transverse direction can be enhanced

**Figure 19.** (a) The unit cell of multiaxis 3D braided preform [77]; multiaxis 3D braided para-aramid preforms (b) cylin‐

Wunner [32] developed a multiaxis warp knit machine for Liba GmbH. The machine uses a total of four yarn sets such as ±bias, warp and filling. These yarn sets are placed as separate layers and these layers are locked by stitching yarn by using tricot pattern, as

drical Kevlar® preform and (c) conic Kevlar® preform [78].

*3.2.9. Three-dimensional knitted fabric*

**Figure 20.** Multiaxis warp knit structure [32].

shown in Figure 20.

98 Non-woven Fabrics

and the non-uniformity in the directional Poisson's ratios can be decreased [78].

Multiple layers of 2D nonwoven webs are stacked and stitched together in thickness direction to obtain 3D nonwoven fabric. Stitching yarn provides through-the-thickness reinforcement in an effort to impart out-of-plane structural integrity and reduce delamination failures. Olry developed a method called "Noveltex" for 3D nonwoven preform fabrication [80]. This method uses needle punching as a means of fiber entanglement. A 3D nonwoven preform was developed using hydroentanglement method to create through-thickness fiber insertion. Biaxially reinforced nonwoven fabric is another type of 3D nonwoven preforms that is manufactured by employing warp knitting technology. The preform consists of warp and weft yarns along with a fiber web. Warp and weft yarns can be thought of as inlays such that they are laid in fabric structure as separate layers without any interlacements. Warp yarns, weft yarns and fiber web are all connected by stitching yarns to form an integrated structure as shown in Figure 22 [81]. Geogrid structures can be considered as a special type of nonwoven fabric. They can be classified based on their shape such as uniaxial, biaxial and triaxial geogrid structures used in wall, slope and road applications; and manufacturing methods such as punched and drawn geogrids, coated yarn geogrid and laser welded geogrids. The basic functions of geogrid structures are to interlock the aggregates, to redistribute the load over wider area to reduce the vertical stress, and to provide lateral restraint, improved bearing capacity, and tension membrane effect [82].

**Figure 22.** Three-dimensional nonwoven fabric; (a) schematic view of flat 3D nonwoven preform (left) and 3D PANbased graphite felt composite (right); (b) schematic view of circular 3D nonwoven preform (left) and 3D PAN-based graphite felt composite (right); (c) top and side views of 3D biaxially reinforced nonwoven preform [80, 81, 83].


*2.1. Visual Appearance*

The prepared edible film samples including B (Control), PC20, PC30, and PC40 were visually examined, and it was observed that control (B) and PC20 film samples had superior visual characteristics and was easy to peel from the Petri plate. Moreover, the PC20 film exhibited relatively improved characteristics regarding brittleness, flexibility, and stiffness. Film samples PC30 and PC40 containing 0.15% and 0.2% MOEO, respectively, were less transparent while exhibiting uniformity and flexibility. The better visual characteristics of the PC30 and PC40 film samples could be due to the addition of higher concentrations of MOEO and Tween 80, resulted in the uniform dispersion of the oil in the film matrix (Figure 1).

**Figure 1.** Visual appearance of B (control), PC20 (containing 0.1% MOEO), PC30 (containing 0.15% of MOEO), and PC40 (containing 0.2% MOEO). **Figure 1.** Visual appearance of B (control), PC20 (containing 0.1% MOEO), PC30 (containing 0.15% of MOEO), and PC40 (containing 0.2% MOEO).


Social Inequality - Structure and Social Processes

*Edited by Yaroslava Robles-Bykbaev*

Published in London, United Kingdom

Social Inequality - Structure and Social Processes http://dx.doi.org/10.5772/intechopen.100687 Edited by Yaroslava Robles-Bykbaev

Assistant to the Editor: Jessica Vanessa Quito Calle

Contributors

Andrew Dougall Roberts, Bobo Chazireni, Lucy Chamba, Michael Connolly, Paulette Meikle, Yaroslava Robles-Bykbaev

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**6. Materials and methodology of investigation**

The methodology of the study implies the investigation of the levels of indoor air contamina‐ tion with fungi and bacteria. The destination of the investigated indoor spaces was very diverse. The levels of bacteriological contamination were assessed in indoor spaces designated as living spaces, social use spaces like shops, and work and education spaces like schools, offices, and archives.

Quantitative and qualitative assessments were made to evaluate the levels of air contamina‐ tion. Passive and active methods were used to sample the air from the investigated sites. The results were compared to the enforced recommendations in Romania, at this moment.

#### **6.1. Investigated places**

#### *6.1.1. Houses in Bucharest City*

There were 10 investigated apartments, with different numbers of room situated at different floors and with different geographical orientations and different characteristics.

Type of investigated apartments:

For an accurate assessment of the biological contamination of indoor air, the measurements were made three times in a year, in the winter, in the summer, and in the autumn, 3 days consecutively, in the kitchen and in the living room of each investigated apartment.

#### *6.1.2. Schools*

There were two investigated schools, one in Bucharest and one in Giurgiu City.

In the school in Bucharest, the investigations were made

In the school in Giurgiu, investigations were made

*6.1.3. Offices*

Fungi were investigated in eight offices.

#### *6.1.4. Archives*

Fungi were investigated in 10 archive rooms, where the possibility of contamination was presumed.

#### *6.1.5. Shops*

The levels of bacteria in air were investigated (a) in a supermarket in Bucharest City with ground floor and 4 floors and (b) in a shop with only a ground floor.

The investigations were made in the morning when the shop opens and in the evening when the shops were nearly to be closed.

#### **6.2. Sampling methods**

*6.1.2. Schools*

496 Current Air Quality Issues

the school library

**•** in the hall of the school

**•** in a classroom at 8:00 a.m.

**•** in a classroom at 11:00 a.m.

**•** in a classroom at 1:00 p.m.

Fungi were investigated in eight offices.

the shops were nearly to be closed.

**•** in the school hall.

*6.1.3. Offices*

*6.1.4. Archives*

presumed.

*6.1.5. Shops*

There were two investigated schools, one in Bucharest and one in Giurgiu City.

**•** during the school day (at 9.30 a.m.), in a classroom with 23 children

**•** in study room number 1, next to the library with 15 children inside

**•** in study room number 2, next to the library, room that is connected to the hall in front of

Fungi were investigated in 10 archive rooms, where the possibility of contamination was

The levels of bacteria in air were investigated (a) in a supermarket in Bucharest City with

The investigations were made in the morning when the shop opens and in the evening when

ground floor and 4 floors and (b) in a shop with only a ground floor.

**•** in a classroom (at 11:00 a.m.) with 24 children and 1 teacher

**•** in the school library (at 10:00 a.m.) with 3 people inside

**•** in the medical ward, with 8 people inside the room. In the school in Giurgiu, investigations were made

In the school in Bucharest, the investigations were made

**•** in an empty classroom, before 8:00 a.m.

**•** in a classroom with 36 children and 1 teacher

**•** in a classroom with 23 children and 1 teacher

For the bacteriological and fungal evaluation, the sampling methods used were a passive method and an active one [26, 27].

#### *6.2.1. Passive sampling method*

The total number of mesophilic aerobic bacteria, yeast, and molds in the air of selected rooms was determined using the Koch sedimentation method. Bacteria from air were harvested using the gravitational method on the Petri plates with specific growth media. The measurements of levels of bacteria in air were made in several points on each location. The results were expressed as CFU/m3 . The equation used for calculating the results was as follows:

3 CFU/m = × 10,000/s · /5 *n t*

where n is the number of colonies on the Petri plate, s is the surface of the Petri plate, and *t* is the time of Petri plate exposure. Petri dishes with growth media were exposed for 10 min.

Results obtained by the Koch sedimentation method are less accurate than those from impaction methods with the use of an air sampler. The sedimentation method does not require expensive instrumentation and is cheap and simple. The sedimentation method does not permit exact quantitative determination. Still, this method permit an assessment of the types of microorganisms present in the air and can give an approximation of bacterial and fungal concentration in the air of investigated indoor spaces.

#### *6.2.2. Active sampling method*

This method used an impactor sampler M.A.S.Q.-90, with 2 l/min airflow and a total air volume equal to 200 l.

The following growth media were used [28]:

To obtain the total number of bacteria in the air of investigated rooms, the Petri dishes with growth media were incubated at 37°C for 24 h and then left for another 24 h at room temper‐ ature. To obtain the number of fungi colonies, the Petri dishes were exposed for 10 days at 25°C.

The identification of bacteria types was made by macroscopic estimation (description of colony) and by microscopic estimation.

Diagnosis of filamentous fungi was based on estimation of morphological features of growth on Czapek medium as well as on microscopic observation.

#### **6.3. Indicators used to assessed the air contamination of indoor air**

The following were assessed and counted on a Petri dish:

#### **6.4. References values**

To have a good assessment of the results that were obtained, it was useful to have as references standardized values for the biological contamination of indoor air.

At present, there are no standards or guidelines for microbiological quality of indoor air. Each country has individual regulations and recommendations.

The Commission of the European Communities (CEC) defines the following level of bacterio‐ logical air contamination:0 undetectable, 1-499 CFU/m3 low, 500-999 CFU/m3 medium, >1, 000 CFU/m3 high, and 2, 000 CFU/m3 very high. A value of 10, 000 CFU/m3 of nondescript air‐ borne microbes could therefore be considered a hazardous level for indoor environment [12].

In 2001, the American Industrial Hygiene Association (AIHA) published a proposition of guidelines that stipulate 1, 000 of viable CFU/m3 in air as an upper limit for concentration in indoor environment and for the amount of fungal spores in different indoor environments. It is proposed that the indoor air levels of microorganisms should be less than 500 CFU/m3 for residential buildings and less than 250 CFU/m3 for commercial buildings [29, 30].

According to current Swedish requirements, the number of 500 colony-forming units (CFU) of bacteria and 300 CFU of fungal spores in 1 m3 can be accepted in an indoor environment [31].

The regulation in Brazil stipulates that the upper limit for total amount of airborne microor‐ ganisms (especially fungi) should be 750 CFU/m3 [32, 33].

In Hong Kong, the indoor air levels of microorganism should be less than 1, 000 CFU/m3 of bacteria. The levels are considered excellent if they are less than 500 CFU/m3 [34, 35].

In Taiwan, the indoor air levels of bacteria should not exceeded 500 CFU/m3 and 1, 000 CFU/m3 for fungi [36, 37].

In India, there are no guidelines for the levels of bacteriological contamination of air [38].

In Singapore, the regulation for indoor air quality imposes an upper limit for the concentration of bacteria of 500 CFU/m3 [39].

and 550 CFU/m3

, the minimum value was

for bacteria at 37. The

for

The American Conference of Governmental Industrial Hygienists (ACGIH) stipulates that the counts for total bacteria should not exceed 500 CFU/m3 [40].

WHO (1988) has set a limitation of fungi counts at 500 CFU/m3 [41].

The Korean Indoor Air Quality Control in Public Use Facilities Act prescribes a total airborne bacterial concentration below 800 CFU/m3 for hospitals, nurseries, public nurseries, and hospitals for the elderly and post partum care centers [42, 43]. In Singapore, the regulation for indoor air quality imposes an upper limit for the concentration of bacteria of 500

In Romania, the reference value for indoor air contamination in a house is equal to 2, 500 CFU/ m3 and 550 CFU/m3 for fungi. In schools, the level of bacteriological contamination of air should not exceed 1, 500 CFU/m3 for bacteria at 37°. The presence of coliform bacteria and hemolytic bacteria are not allowed in air inside education units. CFU/m3 [39]. The American Conference of Governmental Industrial Hygienists (ACGIH) stipulates that the counts for total bacteria should not exceed 500 CFU/m 3 [40]. WHO (1988) has set a limitation of fungi counts at 500 CFU/m 3 [41].

for hospitals, nurseries, public nurseries, and hospitals for the elderly and post

The Korean Indoor Air Quality Control in Public Use Facilities Act prescribes a total airborne bacterial

In Romania, the reference value for indoor air contamination in a house is equal to 2,500 CFU/m3

#### **7. Results** partum care centers [42,43].

**6.3. Indicators used to assessed the air contamination of indoor air**

**c.** The presence of *Staphylococcus aureus* and non-*aureus* with and without hemolytic

**d.** The presence of coliform bacteria as an indicator of enteric contamination of air

**f.** The types of fungi as a qualitative assessment of indoor air contamination with fungi

To have a good assessment of the results that were obtained, it was useful to have as references

At present, there are no standards or guidelines for microbiological quality of indoor air. Each

The Commission of the European Communities (CEC) defines the following level of bacterio‐

CFU/m3 high, and 2, 000 CFU/m3 very high. A value of 10, 000 CFU/m3 of nondescript air‐ borne microbes could therefore be considered a hazardous level for indoor environment [12]. In 2001, the American Industrial Hygiene Association (AIHA) published a proposition of guidelines that stipulate 1, 000 of viable CFU/m3 in air as an upper limit for concentration in indoor environment and for the amount of fungal spores in different indoor environments. It is proposed that the indoor air levels of microorganisms should be less than 500 CFU/m3

According to current Swedish requirements, the number of 500 colony-forming units (CFU)

The regulation in Brazil stipulates that the upper limit for total amount of airborne microor‐

In Hong Kong, the indoor air levels of microorganism should be less than 1, 000 CFU/m3 of

In India, there are no guidelines for the levels of bacteriological contamination of air [38].

In Singapore, the regulation for indoor air quality imposes an upper limit for the concentration

bacteria. The levels are considered excellent if they are less than 500 CFU/m3

In Taiwan, the indoor air levels of bacteria should not exceeded 500 CFU/m3

[32, 33].

(colony-forming units/cubic meter,

as quantitative assessment of contami‐

medium, >1, 000

[34, 35].

and 1, 000

for

low, 500-999 CFU/m3

for commercial buildings [29, 30].

can be accepted in an indoor environment [31].

The following were assessed and counted on a Petri dish: **a.** The total number of bacteria, expressed in CFU/m3

**b.** The presence of *Streptococcus* alpha and beta hemolytic

standardized values for the biological contamination of indoor air.

country has individual regulations and recommendations.

logical air contamination:0 undetectable, 1-499 CFU/m3

residential buildings and less than 250 CFU/m3

of bacteria and 300 CFU of fungal spores in 1 m3

ganisms (especially fungi) should be 750 CFU/m3

[39].

for fungi [36, 37].

of bacteria of 500 CFU/m3

**e.** The total number of fungi expressed in CFU/m3

CFU/m3 )

498 Current Air Quality Issues

proprieties

**6.4. References values**

nation

CFU/m3

#### **7.1. Houses in Bucharest City** fungi. In schools, the level of bacteriological contamination of air should not exceed 1,500 CFU/m3 presence of coliform bacteria and hemolytic bacteria are not allowed in air inside education units.

concentration below 800 CFU/m3

The highest value for air contamination with germs at 37°C was equal to 21, 466 CFU/m3 in one of the apartments with one room (apartment B). **7. Results 7.1. Houses in Bucharest City**

In the kitchens, the highest value of contamination identified was equal to 12, 701 CFU/m3 , the minimum value was equal to 201 CFU/m3 , and the highest average value was equal 3, 200 CFU/ m3 (Fig. 1). The highest value for air contamination with germs at 37C was equal to 21,466 CFU/m3 in one of the apartments with one room (apartment B). In the kitchens, the highest value of contamination identified was equal to 12,701 CFU/m3 , the minimum value was equal to 201 CFU/m3 , and the highest average value was equal 3,200 CFU/m3 (Fig. 1).

at 37C).

(Fig. 2).

In the living rooms, the highest identified value of contamination was equal to 21,466 CFU/m3 equal to 210 CFU/m3 , and the highest average value was equal 4,731 CFU/m3 **Figure 1.** Bacteriological contamination of kitchen air (CFU/m3 at 37°C).

Fig. 1. Bacteriological contamination of kitchen air (CFU/m3

CFU/m3

**7. Results**

room (apartment B).

to 201 CFU/m3

[39].

bacteria should not exceed 500 CFU/m

concentration below 800 CFU/m3

**7.1. Houses in Bucharest City**

partum care centers [42,43].

In the living rooms, the highest identified value of contamination was equal to 21, 466 CFU/m3 , the minimum value was equal to 210 CFU/m3 , and the highest average value was equal 4, 731 CFU/m3 (Fig. 2). Fig. 1. Bacteriological contamination of kitchen air (CFU/m3 at 37C). In the living rooms, the highest identified value of contamination was equal to 21,466 CFU/m3 , the minimum value was equal to 210 CFU/m3 , and the highest average value was equal 4,731 CFU/m3 (Fig. 2).

In Singapore, the regulation for indoor air quality imposes an upper limit for the concentration of bacteria of 500

The American Conference of Governmental Industrial Hygienists (ACGIH) stipulates that the counts for total

The Korean Indoor Air Quality Control in Public Use Facilities Act prescribes a total airborne bacterial

In Romania, the reference value for indoor air contamination in a house is equal to 2,500 CFU/m3

fungi. In schools, the level of bacteriological contamination of air should not exceed 1,500 CFU/m3

presence of coliform bacteria and hemolytic bacteria are not allowed in air inside education units.

The highest value for air contamination with germs at 37C was equal to 21,466 CFU/m3

In the kitchens, the highest value of contamination identified was equal to 12,701 CFU/m3

, and the highest average value was equal 3,200 CFU/m3

3 [41].

for hospitals, nurseries, public nurseries, and hospitals for the elderly and post

(Fig. 1).

and 550 CFU/m3

in one of the apartments with one

, the minimum value was equal

for bacteria at 37. The

for

3 [40].

WHO (1988) has set a limitation of fungi counts at 500 CFU/m

**Figure 2.** Levels of bacteriological air contamination in living rooms (CFU/m3 at 37°C).

Taking into account the moment of the day, the highest bacteriological contamination (average values) was found in the living room, in the evening, and was equal to 6, 665 CFU/m3 in the one room apartment—apartment B (Fig. 3).

at 37C).

in one room

Summer kitchen Summer living room Fall kitchen Fall living room Winter kitchen Winter living room

Fig. 3. Average numbers of germs at 37C in the morning and in the evening in investigated apartments. The indoor air contamination is higher in the fall and in the winter, reaching a level equal to 7,853 CFU/m3 **Figure 3.** Average numbers of germs at 37°C in the morning and in the evening in investigated apartments.

Fig. 4. Average numbers of germs at 37C according to the season in investigated apartments.

In connection to the smoking habit of the inhabitants, apartments with one and two rooms had a higher bacteriological

apartment—apartment B (Fig. 4).

contamination of air (Fig. 5).

The indoor air contamination is higher in the fall and in the winter, reaching a level equal to 7, 853 CFU/m3 in one room apartment (average values)—apartment B (Fig. 4).

In the living rooms, the highest identified value of contamination was equal to 21, 466

Taking into account the moment of the day, the highest bacteriological contamination (average values) was found in the living room, in the evening, and was equal to 6, 665 CFU/m3

Fig. 3. Average numbers of germs at 37C in the morning and in the evening in investigated apartments. The indoor air contamination is higher in the fall and in the winter, reaching a level equal to 7,853 CFU/m3

**Figure 3.** Average numbers of germs at 37°C in the morning and in the evening in investigated apartments.

Fig. 4. Average numbers of germs at 37C according to the season in investigated apartments.

In connection to the smoking habit of the inhabitants, apartments with one and two rooms had a higher bacteriological

at 37C).

In Singapore, the regulation for indoor air quality imposes an upper limit for the concentration of bacteria of 500

The American Conference of Governmental Industrial Hygienists (ACGIH) stipulates that the counts for total

The Korean Indoor Air Quality Control in Public Use Facilities Act prescribes a total airborne bacterial

In Romania, the reference value for indoor air contamination in a house is equal to 2,500 CFU/m3

fungi. In schools, the level of bacteriological contamination of air should not exceed 1,500 CFU/m3

presence of coliform bacteria and hemolytic bacteria are not allowed in air inside education units.

The highest value for air contamination with germs at 37C was equal to 21,466 CFU/m3

In the kitchens, the highest value of contamination identified was equal to 12,701 CFU/m3

, and the highest average value was equal 3,200 CFU/m3

3 [41].

for hospitals, nurseries, public nurseries, and hospitals for the elderly and post

(Fig. 1).

(Fig. 2).

at 37C).

at 37°C).

3 [40].

WHO (1988) has set a limitation of fungi counts at 500 CFU/m

, and the highest average value was

, the minimum value was

Minimum values Maximum values Average values

in the

in one room

Summer kitchen Summer living room Fall kitchen Fall living room Winter kitchen Winter living room

Morning ‐ kitchen Morning ‐ living rooms Evening Kitchen Evening ‐ living rooms

in one of the apartments with one

, the minimum value was equal

and 550 CFU/m3

for bacteria at 37. The

for

, the minimum value was equal to 210 CFU/m3

Fig. 2. Levels of bacteriological air contamination in living rooms (CFU/m3

**Figure 2.** Levels of bacteriological air contamination in living rooms (CFU/m3

one room apartment—apartment B (Fig. 3).

apartment—apartment B (Fig. 4).

contamination of air (Fig. 5).

In the living rooms, the highest identified value of contamination was equal to 21,466 CFU/m3

, and the highest average value was equal 4,731 CFU/m3

Fig. 1. Bacteriological contamination of kitchen air (CFU/m3

(Fig. 2).

CFU/m3

500 Current Air Quality Issues

equal 4, 731 CFU/m3

equal to 210 CFU/m3

0

5000

10000

15000

20000

25000

CFU/m3

**7. Results**

room (apartment B).

to 201 CFU/m3

[39].

bacteria should not exceed 500 CFU/m

concentration below 800 CFU/m3

**7.1. Houses in Bucharest City**

partum care centers [42,43].

In connection to the smoking habit of the inhabitants, apartments with one and two rooms had a higher bacteriological **Figure 4.** Average numbers of germs at 37°C according to the season in investigated apartments.

contamination of air (Fig. 5).

contamination of air (Fig. 5).

3500

Fig. 4. Average numbers of germs at 37C according to the season in investigated apartments.

4000 In connection to the smoking habit of the inhabitants, apartments with one and two rooms had a higher bacteriological contamination of air (Fig. 5). Fig. 4. Average numbers of germs at 37C according to the season in investigated apartments.

In connection to the smoking habit of the inhabitants, apartments with one and two rooms had a higher bacteriological

2‐Room Apartment with linked

2‐Room Apartment with linked

2‐Room Apartment with linked

2‐Room Apartment with separate rooms C

rooms E

rooms F

rooms D

In connection to the type of apartment, bacteriological contamination is higher in the apartments with two linked rooms. **Figure 5.** Number of germs at 37°C in houses according to the smoking habit.

Fig. 5. Number of germs at 37C in houses according to the smoking habit.

Bacteriological contamination is also presented in the three-room apartment (Figs. 6 and 7).

Fig. 6. Number of germs at 37C and the type of apartment (apartments with two rooms).

Kitchen Living Room

In connection to the type of apartment, bacteriological contamination is higher in the apart‐ ments with two linked rooms. Bacteriological contamination is also presented in the threeroom apartment (Figs. 6 and 7). Fig. 5. Number of germs at 37C in houses according to the smoking habit. In connection to the type of apartment, bacteriological contamination is higher in the apartments with two linked rooms.

In connection to the smoking habit of the inhabitants, apartments with one and two rooms had a higher bacteriological

Fig. 4. Average numbers of germs at 37C according to the season in investigated apartments.

Bacteriological contamination is also presented in the three-room apartment (Figs. 6 and 7).

**Figure 6.** Number of germs at 37°C and the type of apartment (apartments with two rooms).

Fig. 6. Number of germs at 37C and the type of apartment (apartments with two rooms).

Fig. 6. Number of germs at 37C and the type of apartment (apartments with two rooms).

Contamination of air with staphylococci reached the highest value equal to 7,988 CFU/m3 in the kitchen and 10,157 **Figure 7.** Number of germs at 37°C and the type of apartment (apartments with three rooms).

in the living room (Figs. 8 and 9).

CFU/m3

Fig. 7. Number of germs at 37C and the type of apartment (apartments with three rooms).

Contamination of air with staphylococci reached the highest value equal to 7, 988 CFU/m3 in the kitchen and 10, 157 CFU/m3 in the living room (Figs. 8 and 9).

).

Fig. 4. Average numbers of germs at 37C according to the season in investigated apartments.

Fig. 5. Number of germs at 37C in houses according to the smoking habit.

Bacteriological contamination is also presented in the three-room apartment (Figs. 6 and 7).

Fig. 6. Number of germs at 37C and the type of apartment (apartments with two rooms).

contamination of air (Fig. 5).

CFU/m3

in the living room (Figs. 8 and 9).

In connection to the smoking habit of the inhabitants, apartments with one and two rooms had a higher bacteriological

In connection to the type of apartment, bacteriological contamination is higher in the apartments with two linked rooms.

**Figure 8.** Contamination with staphylococci in kitchen air (CFU/m3 ).

Fig. 8. Contamination with staphylococci in kitchen air (CFU/m3

In connection to the type of apartment, bacteriological contamination is higher in the apart‐ ments with two linked rooms. Bacteriological contamination is also presented in the three-

In connection to the type of apartment, bacteriological contamination is higher in the apartments with two linked rooms.

In connection to the smoking habit of the inhabitants, apartments with one and two rooms had a higher bacteriological

In connection to the type of apartment, bacteriological contamination is higher in the apartments with two linked rooms.

2‐Room Apartment with linked

2‐Room Apartment with linked

2‐Room Apartment with linked

3‐Room Apartment with linked

3‐Room Apartment with linked

3‐Room Apartment with linked

3‐Room Apartment with separate rooms G

rooms I

rooms J

rooms H

in the kitchen and 10,157

in

2‐Room Apartment with separate rooms C

rooms E

rooms F

rooms D

In connection to the smoking habit of the inhabitants, apartments with one and two rooms had a higher bacteriological

Fig. 4. Average numbers of germs at 37C according to the season in investigated apartments.

Fig. 5. Number of germs at 37C in houses according to the smoking habit.

Bacteriological contamination is also presented in the three-room apartment (Figs. 6 and 7).

Fig. 4. Average numbers of germs at 37C according to the season in investigated apartments.

Fig. 6. Number of germs at 37C and the type of apartment (apartments with two rooms).

Fig. 6. Number of germs at 37C and the type of apartment (apartments with two rooms).

Fig. 7. Number of germs at 37C and the type of apartment (apartments with three rooms). Contamination of air with staphylococci reached the highest value equal to 7,988 CFU/m3

Contamination of air with staphylococci reached the highest value equal to 7, 988 CFU/m3

in the living room (Figs. 8 and 9).

**Figure 7.** Number of germs at 37°C and the type of apartment (apartments with three rooms).

Kitchen Living Room

Bacteriological contamination is also presented in the three-room apartment (Figs. 6 and 7).

**Figure 6.** Number of germs at 37°C and the type of apartment (apartments with two rooms).

Kitchen Living Room

Fig. 5. Number of germs at 37C in houses according to the smoking habit.

room apartment (Figs. 6 and 7).

contamination of air (Fig. 5).

CFU/m3

the kitchen and 10, 157 CFU/m3

0

500

1000

1500

2000

2500

3000

in the living room (Figs. 8 and 9).

contamination of air (Fig. 5).

502 Current Air Quality Issues

The ratio between staphylococci/germs at 37C is around 40% in the kitchen and over 50% in the living rooms (Fig. 10). **Figure 9.** Contamination with staphylococci in living rooms air (CFU/m).

Fig. 9. Contamination with staphylococci in living rooms air (CFU/m).

Fig. 10. Ratio between staphylococci/germs at 37 in investigated houses.

*niger*, *Rhizopus*, *Mucor*, *Cladosporium herbarum*, and *Fusarium*.

children and 1 teacher, the value identified was 140 CFU/m3

measured were 2,802 CFU/m3

0 10

and 10,859 CFU/m3

7.2. Schools

CFU/m3

was 25 CFU/m3

The ratio between staphylococci/germs at 37°C is around 40% in the kitchen and over 50% in the living rooms (Fig. 10).

20 30 40 50 60 The assessment of the fungal contamination showed that in many homes, the level of fungal contamination was very high in all investigated rooms. The levels of fungi were between 635 and 10, 859 CFU/m3 . The highest values measured were 2, 802 CFU/m3 in the living room, 2,

The assessment of the fungal contamination showed that in many homes, the level of fungal contamination was

% in the kitchen % in the living room

In a school in Bucharest, the following results were obtained: in an empty classroom, before 8:00 a.m., the value identified

in the kitchen, 7,675 CFU/m3

; in a classroom (at 11:00 a.m.) with 24 children and 1 teacher,

in the bedroom (highest overall). The species of fungi identified were *Penicillium*, *Aspergillus*

; during the school day (at 9.30:00 a.m.) in a classroom with 23 children, the value identified was 67

. The highest values

in the bathroom,

% in the kitchen % in the living room

; in a classroom with 23

very high in all investigated rooms. The levels of fungi were between 635 and 10,859 CFU/m3

in the living room, 2,302 CFU/m3

; in a classroom with 36 children and 1 teacher, the value identified was 229 CFU/m3

**Figure 10.** Ratio between staphylococci/germs at 37° in investigated houses.

Fig. 10. Ratio between staphylococci/germs at 37 in investigated houses.

302 CFU/m3 in the kitchen, 7, 675 CFU/m3 in the bathroom, and 10, 859 CFU/m3 in the bedroom (highest overall). The species of fungi identified were *Penicillium*, *Aspergillus niger*, *Rhizopus*, *Mucor*, *Cladosporium herbarum*, and *Fusarium*. very high in all investigated rooms. The levels of fungi were between 635 and 10,859 CFU/m3 . The highest values measured were 2,802 CFU/m3 in the living room, 2,302 CFU/m3 in the kitchen, 7,675 CFU/m3 in the bathroom, and 10,859 CFU/m3 in the bedroom (highest overall). The species of fungi identified were *Penicillium*, *Aspergillus niger*, *Rhizopus*, *Mucor*, *Cladosporium herbarum*, and *Fusarium*.

; during the school day (at 9.30:00 a.m.) in a classroom with 23 children, the value identified was 67

The assessment of the fungal contamination showed that in many homes, the level of fungal contamination was

#### **7.2. Schools** 7.2. Schools In a school in Bucharest, the following results were obtained: in an empty classroom, before 8:00 a.m., the value identified was 25 CFU/m3

In a school in Bucharest, the following results were obtained: in an empty classroom, before 8:00 a.m., the value identified was 25 CFU/m3 ; during the school day (at 9.30:00 a.m.) in a classroom with 23 children, the value identified was 67 CFU/m3 ; in a classroom with 36 children and 1 teacher, the value identified was 229 CFU/m3 ; in a classroom with 23 children and 1 teacher, the value identified was 140 CFU/m3 ; in a classroom (at 11:00 a.m.) with 24 children and 1 teacher, the value identified was 1, 465 CFU/m3 with 38 CFU/m3 *Streptococcus* alphahemolytic present; in the school library (at 10:00 a.m.) with 3 people inside, the biological contamination of air was equal to 45 CFU/m3 ; in study room number 1, next to the library with 15 children inside, the level of air contamination was equal to 89 CFU/m3 ; in study room number 2, next to the library, the room connected to the hall in front of the school library, the level of biological air contamination was equal to 427 CFU/m3 ; the level of air contamination in the school hall was equal to 180 CFU/m3 ; in the medical ward, with 8 people inside the room, the level of biological air contamination was equal to 60 CFU/m3 , 1 CFU/m3 *Streptococcus* alpha hemolytic, and 1 CFU/m3 *Streptococcus* beta hemolytic present (Fig.11). CFU/m3 ; in a classroom with 36 children and 1 teacher, the value identified was 229 CFU/m3 ; in a classroom with 23 children and 1 teacher, the value identified was 140 CFU/m3 ; in a classroom (at 11:00 a.m.) with 24 children and 1 teacher, the value identified was 1,465 CFU/m3 with 38 CFU/m3 *Streptococcus* alpha-hemolytic present; in the school library (at 10:00 a.m.) with 3 people inside, the biological contamination of air was equal to 45 CFU/m3 ; in study room number 1, next to the library with 15 children inside, the level of air contamination was equal to 89 CFU/m3 ; in study room number 2, next to the library, the room connected to the hall in front of the school library, the level of biological air contamination was equal to 427 CFU/m3 ; the level of air contamination in the school hall was equal to 180 CFU/m3 ; in the medical ward, with 8 people inside the room, the level of biological air contamination was equal to 60 CFU/m3 , 1 CFU/m3 *Streptococcus* alpha hemolytic, and 1 CFU/m3 *Streptococcus* beta hemolytic present (Fig.11).

In a school in Giurgiu City, the following results were obtained: at 8:00 a.m., a number of 1, 259 CFU/m3 was identified; at 11:00 a.m., a number of 1, 732 CFU/m3 was identified; at 1:00 p.m., a level of biological air contamination equal to 472 CFU/m3 was identified. In the school hall, the level of air contamination was equal to 2, 202 CFU/m3 , the level of contamination of the air with *S.* non-*aureus* was equal to 1, 259 CFU/m3 at 8:00 a.m. and 11:00 a.m., and 472 CFU/ m3 at 1:00 p.m.; the level of contamination with *Streptococcus* alpha-hemolytic was equal to 157

; in a classroom with 23

; in study room number 1,

was identified; at

, 1 CFU/m3 *Streptococcus* alpha

; in a classroom (at 11:00 a.m.) with 24 children and 1 teacher,

with 38 CFU/m3 *Streptococcus* alpha-hemolytic present; in the school library (at

In a school in Bucharest, the following results were obtained: in an empty classroom, before 8:00 a.m., the value identified

; in a classroom with 36 children and 1 teacher, the value identified was 229 CFU/m3

10:00 a.m.) with 3 people inside, the biological contamination of air was equal to 45 CFU/m3

8 people inside the room, the level of biological air contamination was equal to 60 CFU/m3

hemolytic, and 1 CFU/m3 *Streptococcus* beta hemolytic present (Fig.11).

; during the school day (at 9.30:00 a.m.) in a classroom with 23 children, the value identified was 67

In a school in Giurgiu City, the following results were obtained: at 8:00 a.m., a number of 1,259 CFU/m3 **Figure 11.** Level of bacteriological contamination of air in a school in Bucharest City.

Fig. 11. Level of bacteriological contamination of air in a school in Bucharest City.

Fig. 11. Level of bacteriological contamination of air in a school in Bucharest City.

can be dangerous for children health (Fig.12).

11:00 a.m., a number of 1,732 CFU/m3

7.2. Schools

CFU/m3

was 25 CFU/m3

equal to 427 CFU/m3

children and 1 teacher, the value identified was 140 CFU/m3

the value identified was 1,465 CFU/m3

CFU/m3 at 11:00 a.m. In a classroom and in the school hall, at 1:00 p.m., coliform bacteria were identified as an indicator of enteric contamination of air. The level of contamination with coliform bacteria was equal to 315 CFU/m3 . The detected levels of coliform bacteria can be dangerous for children health (Fig.12). In a school in Giurgiu City, the following results were obtained: at 8:00 a.m., a number of 1,259 CFU/m3 was identified; at 11:00 a.m., a number of 1,732 CFU/m3 was identified; at 1:00 p.m., a level of biological air contamination equal to 472 CFU/m3 was identified. In the school hall, the level of air contamination was equal to 2,202 CFU/m3 , the level of contamination of the air with *S.* non-*aureus* was equal to 1,259 CFU/m3 at 8:00 a.m. and 11:00 a.m., and 472 CFU/m3 at 1:00 p.m.; the level of contamination with *Streptococcus* alpha-hemolytic was equal to 157 CFU/m3 at 11:00 a.m. In a classroom and in the school hall, at 1:00 p.m., coliform bacteria were identified as an indicator of enteric contamination of air. The level of contamination with coliform bacteria was equal to 315 CFU/m3 . The detected levels of coliform bacteria CFU/m3 was identified. In the school hall, the level of air contamination was equal to 2,202 CFU/m3 , the level of contamination of the air with *S.* non-*aureus* was equal to 1,259 CFU/m3 at 8:00 a.m. and 11:00 a.m., and 472 CFU/m3 at 1:00 p.m.; the level of contamination with *Streptococcus* alpha-hemolytic was equal to 157 CFU/m3 at 11:00 a.m. In a classroom and in the school hall, at 1:00 p.m., coliform bacteria were identified as an indicator of enteric contamination of air. The level of contamination with coliform bacteria was equal to 315 CFU/m3 . The detected levels of coliform bacteria can be dangerous for children health (Fig.12).

was identified; at 1:00 p.m., a level of biological air contamination equal to 472

**7.3. Offices**  The levels of fungal contamination were between 734 and 4,034 CFU/m3 . The fungi species identified were *Penicillium* spp., *A. niger*, *A. flavus*, *Aspergillus versicolor*, *Mucor*, and *Cladosporium*. **Figure 12.** Levels of bacteriological air contamination in a school in Giurgiu City.

pollution with fungi, the following types of fungi were identified: *Penicillium* spp., *A. niger*, *A. flavus*, *A.*

Fig. 12. Levels of bacteriological air contamination in a school in Giurgiu City.

#### **7.4. Archives**  The levels of fungal contamination were between 655 and 1,490 CFU/m3 **7.3. Offices**

302 CFU/m3

**7.2. Schools**

259 CFU/m3

m3

in the kitchen, 7, 675 CFU/m3

*niger*, *Rhizopus*, *Mucor*, *Cladosporium herbarum*, and *Fusarium*.

**Figure 10.** Ratio between staphylococci/germs at 37° in investigated houses.

*Mucor*, *Cladosporium herbarum*, and *Fusarium*.

measured were 2,802 CFU/m3

504 Current Air Quality Issues

and 10,859 CFU/m3

7.2. Schools

CFU/m3

was 25 CFU/m3

equal to 427 CFU/m3

8:00 a.m., the value identified was 25 CFU/m3

the value identified was 1,465 CFU/m3

teacher, the value identified was 140 CFU/m3

contamination of air was equal to 45 CFU/m3

in the school hall was equal to 180 CFU/m3

and 1 teacher, the value identified was 229 CFU/m3

children and 1 teacher, the value identified was 140 CFU/m3

and 1 teacher, the value identified was 1, 465 CFU/m3

classroom with 23 children, the value identified was 67 CFU/m3

hemolytic, and 1 CFU/m3 *Streptococcus* beta hemolytic present (Fig.11).

Fig. 10. Ratio between staphylococci/germs at 37 in investigated houses.

level of biological air contamination was equal to 427 CFU/m3

the level of biological air contamination was equal to 60 CFU/m3

hall, the level of air contamination was equal to 2, 202 CFU/m3

the air with *S.* non-*aureus* was equal to 1, 259 CFU/m3

hemolytic, and 1 CFU/m3 *Streptococcus* beta hemolytic present (Fig.11).

(highest overall). The species of fungi identified were *Penicillium*, *Aspergillus niger*, *Rhizopus*,

in the bedroom (highest overall). The species of fungi identified were *Penicillium*, *Aspergillus*

; during the school day (at 9.30:00 a.m.) in a classroom with 23 children, the value identified was 67

The assessment of the fungal contamination showed that in many homes, the level of fungal contamination was

% in the kitchen % in the living room

very high in all investigated rooms. The levels of fungi were between 635 and 10,859 CFU/m3

in the living room, 2,302 CFU/m3

; in a classroom with 36 children and 1 teacher, the value identified was 229 CFU/m3

In a school in Bucharest, the following results were obtained: in an empty classroom, before

In a school in Bucharest, the following results were obtained: in an empty classroom, before 8:00 a.m., the value identified

hemolytic present; in the school library (at 10:00 a.m.) with 3 people inside, the biological

next to the library, the room connected to the hall in front of the school library, the level of biological air contamination was

; the level of air contamination in the school hall was equal to 180 CFU/m3

number 2, next to the library, the room connected to the hall in front of the school library, the

In a school in Giurgiu City, the following results were obtained: at 8:00 a.m., a number of 1,

p.m., a level of biological air contamination equal to 472 CFU/m3 was identified. In the school

at 1:00 p.m.; the level of contamination with *Streptococcus* alpha-hemolytic was equal to 157

was identified; at 11:00 a.m., a number of 1, 732 CFU/m3 was identified; at 1:00

15 children inside, the level of air contamination was equal to 89 CFU/m3

10:00 a.m.) with 3 people inside, the biological contamination of air was equal to 45 CFU/m3

next to the library with 15 children inside, the level of air contamination was equal to 89 CFU/m3

8 people inside the room, the level of biological air contamination was equal to 60 CFU/m3

in the bathroom, and 10, 859 CFU/m3 in the bedroom

in the kitchen, 7,675 CFU/m3

with 38 CFU/m3 *Streptococcus* alpha-hemolytic present; in the school library (at

; during the school day (at 9.30:00 a.m.) in a

; in a classroom (at 11:00 a.m.) with 24 children and 1 teacher,

; in a classroom (at 11:00 a.m.) with 24 children

; in study room number 1, next to the library with

; in the medical ward, with 8 people inside the room,

; in a classroom with 23 children and 1

with 38 CFU/m3 *Streptococcus* alpha-

, 1 CFU/m3 *Streptococcus* alpha

; in a classroom with 36 children

; in study room number 2,

; in the medical ward, with

; in study room number 1,

; in a classroom with 23

. The highest values

in the bathroom,

% in the kitchen % in the living room

; the level of air contamination

, 1 CFU/m3 *Streptococcus* alpha

, the level of contamination of

at 8:00 a.m. and 11:00 a.m., and 472 CFU/

; in study room

*versicolor*, *Mucor*, *Cladosporium*. The levels of fungal contamination were between 734 and 4, 034 CFU/m3 . The fungi species identified were *Penicillium* spp., *A. niger*, *A. flavus*, *Aspergillus versicolor*, *Mucor*, and *Cladospo‐ rium*.

. By a qualitative assessment of air

#### **7.4. Archives**

The levels of fungal contamination were between 655 and 1, 490 CFU/m3 . By a qualitative assessment of air pollution with fungi, the following types of fungi were identified: *Penicilli‐ um* spp., *A. niger*, *A. flavus*, *A. versicolor*, *Mucor*, *Cladosporium*.

#### **7.5. Shops**

In a supermarket with ground floor and 4 floors, the levels of bacteriological contamination are shown in Table 1.

**Table 1.** Levels of bacteriological air contamination in a supermarket in Bucharest City (M = morning, E = evening)

The highest levels of contamination were registered in the evening and were equal to 13, 836 bacteria at 37°/m3 air at the ground floor and 13, 509 bacteria at 37°/m3 air at the third floor. In the second and fourth floors, in the evening, the level of bacteria in air were equal to 10, 917 bacteria at 37°/m3 air and 10, 352 bacteria at 37°/m3 air. In the first floor, in the evening, levels of bacterial contamination equal to 9, 259 bacteria at 37°/m3 air were registered. The levels of air contamination were the lowest in the first floor (Fig. 13).

floor M 6,155.45 4,276.33 299.75 226.25 182.5 0 E10,916.75 8,705.5 339 286.25 549.16 0

floor M 6,946 4,914.25 280.25 141 273.5 0 E13,509.25 10,800.5 419.5 316 775.5 0

The highest levels of contamination were registered in the evening and were equal to 13,836 bacteria at 37/m3 air at the ground floor and 13**,**509 bacteria at 37/m3 air at the third floor. In the second and fourth floors, in the Fig. 13. Levels of bacteriological contamination of air in a supermarket—average values. The highest levels of contamination were registered in the evening and were equal to 13,836 bacteria at 37/m3 at the ground floor and 13**,**509 bacteria at 37/m3 air at the third floor. In the second and fourth floors, in the **Figure 13.** Levels of bacteriological contamination of air in a supermarket—average values.

the first floor, in the evening, levels of bacterial contamination equal to 9,259 bacteria at 37/m3 air were

 air—average values. The contamination of air, in the case of the supermarket with a ground floor and other four floors, with staphylococci was high in the evening. The highest levels were registered in the third floor with a contamination

the first floor, in the evening, levels of bacterial contamination equal to 9,259 bacteria at 37/m3

registered. The levels of air contamination were the lowest in the first floor (Fig. 13).

registered. The levels of air contamination were the lowest in the first floor (Fig. 13).

evening, the level of bacteria in air were equal to 10,917 bacteria at 37/m3

Fig. 13. Levels of bacteriological contamination of air in a supermarket—average values.

evening, the level of bacteria in air were equal to 10,917 bacteria at 37/m3

**7.4. Archives**

506 Current Air Quality Issues

**7.5. Shops**

**Average values**

Ground floor

Second floor

Fourth floor

bacteria at 37°/m3

are shown in Table 1.

**Total number of bacteria at 37°/m3 air**

The levels of fungal contamination were between 655 and 1, 490 CFU/m3

*um* spp., *A. niger*, *A. flavus*, *A. versicolor*, *Mucor*, *Cladosporium*.

**Total number of staphylococci/m3 air**

assessment of air pollution with fungi, the following types of fungi were identified: *Penicilli‐*

In a supermarket with ground floor and 4 floors, the levels of bacteriological contamination

**Hemolytic staphylococci/m3 air**

M 5, 140.75 3, 296 161.25 197.72 328.75 3.24

E 13, 836.00 10, 397.25 404.5 240.75 964.25 0

E 9, 258.75 7, 316 372 232.5 569.33 0

M 6, 155.45 4, 276.33 299.75 226.25 182.5 0

E 10, 916.75 8, 705.5 339 286.25 549.16 0

E 13, 509.25 10, 800.5 419.5 316 775.5 0

M 6, 473.75 4, 292.75 301.25 189.97 228 0

E 10, 352.25 7, 759.75 405.25 267.78 560 0

**Table 1.** Levels of bacteriological air contamination in a supermarket in Bucharest City (M = morning, E = evening)

air and 10, 352 bacteria at 37°/m3

air contamination were the lowest in the first floor (Fig. 13).

The highest levels of contamination were registered in the evening and were equal to 13, 836 bacteria at 37°/m3 air at the ground floor and 13, 509 bacteria at 37°/m3 air at the third floor. In the second and fourth floors, in the evening, the level of bacteria in air were equal to 10, 917

of bacterial contamination equal to 9, 259 bacteria at 37°/m3 air were registered. The levels of

First floor M 3, 919.25 2, 015.5 137.5 170.55 213.66 0

Third floorM 6, 946 4, 914.25 280.25 141 273.5 0

*Staphylococcus aureus/m3*

 *air*

**α Hemolytic streptococci/m3 air**

air. In the first floor, in the evening, levels

. By a qualitative

**β Hemolytic streptococci/m3 air**

air and 10,352 bacteria at 37/m3

air were

air and 10,352 bacteria at 37/m3

air. In

air

air. In

air, the

air, and the first floor

burden equal to 10,801 bacteria at 37/m3 air, followed by the ground floor with 10,397 bacteria at 37/m3 second floor with 8,706 bacteria at 37/m3 air, the fourth floor with 7,760 bacteria at 37/m3 **Figure 14.** Total number of staphylococci/m3 air—average values.

Fig. 14.Total number of staphylococci/m3

with 7,316 bacteria at 37/m3

Second

Third

Fourth

floor (Fig.14). Hemolytic staphylococci/m3 air and *S. aureus*/m3 air were identified in the same amount in the evening at al floors. α Hemolytic streptococci/m3 air were identified in the highest levels in the evening at the ground floor. In the evening, at the other floors, the levels were almost equal. β Hemolytic streptococci/m3 air were identified at the ground floor only. In a shop with only a ground floor, the levels of bacteriological contamination are presented in Table 2. Table 2. Levels of bacteriological air contamination in a shop with only a ground floor in Bucharest City (M = morning, E = evening) The contamination of air, in the case of the supermarket with a ground floor and other four floors, with staphylococci was high in the evening. The highest levels were registered in the third floor with a contamination burden equal to 10, 801/m3 air, followed by the ground floor with 10, 397/m3 air, the second floor with 8, 706/m3 air, the fourth floor with 7, 760/m3 air, and the first floor with 7, 316/m3 air. It can be seen that the levels of air contamination were the lowest in the first floor (Fig.14).

air. It can be seen that the levels of air contamination were the lowest in the first

Hemolytic staphylococci/m3 air and *S. aureus*/m3 air were identified in the same amount in the evening at al floors.

α Hemolytic streptococci/m3 air were identified in the highest levels in the evening at the ground floor. In the evening, at the other floors, the levels were almost equal.

β Hemolytic streptococci/m3 air were identified at the ground floor only.

In a shop with only a ground floor, the levels of bacteriological contamination are presented in Table 2.

**Table 2.** Levels of bacteriological air contamination in a shop with only a ground floor in Bucharest City (M = morning, E = evening)

Fig. 15. Levels of bacteriological contamination of air in shop with only a ground floor. In the shop with only a ground floor, the levels of air contamination registered higher values in the evening than in the morning measurements for mesophilic bacteria, total number of staphylococci, hemolytic staphylococci, *S.* **Figure 15.** Levels of bacteriological contamination of air in shop with only a ground floor.

contaminations were higher in the fall and winter, especially in the one room apartment—apartment B.

in the living room with average values between 2,406 and 7,903 CFU/m3

The highest levels of staphylococci in air were, in the summer, between 26,125 CFU/m3

in the kitchen and 55,000 CFU/m3

Another study performed in Torun University in Poland [45] had the following results: The levels of air contamination with bacteria were between 7 and 3,313 CFU/m3

. The air pollution with fungi ranged between 0 and 893 CFU/m3

in the bathroom with average values between 4,308 CFU/m3

*aureus*, and α hemolytic streptococci (Fig.15). **8. Discussions** In the investigated houses, the highest value for air contamination with germs, at 37C, was found in one of the apartments with one room (apartment B). Regarding the time of the day, the highest level of bacteriological contamination was found In the shop with only a ground floor, the levels of air contamination registered higher values in the evening than in the morning measurements for mesophilic bacteria, total number of staphylococci, hemolytic staphylococci, *S. aureus*, and α hemolytic streptococci (Fig.15).

in the evening, in the one room apartment—apartment B. Taking into account the time of the year, the indoor air

Considering the smoking habit of the inhabitants, apartments with one and two rooms had a higher bacteriological

The air pollution with fungi was visualized as a layer on the Petri dish in the kitchen and bathroom, and in the living room

in the afternoon. The outdoor fungal background ranged between 217 and 3,750 CFU/m3

Another study that investigated the levels of biological contamination of indoor air in eight libraries of the Jimma

#### contamination of air where there were smokers present. Considering the type of the apartments, in apartments with more than one room, bacteriological contamination is at the **8. Discussions**

and 6,300 CFU/m3

and 6,421 CFU/m3

were between 10,250 CFU/m3

.

CFU/m3

study.

highest level in the apartment with two linked rooms.

the maximum level registered was equal to 1,820 CFU/m3

between 53 and 5,673 CFU/m3

air ranged from 10 to 310 CFU/m3

University in Addis Ababa [46] had the following results:

The levels of bacteria were between 367 and 2,595 CFU/m3

The levels of fungi were between 524 and 1,992 CFU/m3

The ratio between staphylococci/germs at 37C is around 40% in the kitchen and over 50% in the living rooms. In a previous study [44], the levels of bacteriological and fungal pollution of indoor air were as follows: In summer, the maximum levels of bacteria were equal to 57,500 CFU/m3 in the kitchen and bathroom and 32,500 CFU/m3 in the living room with average values between 6,090 and 13,241 CFU/m3 . In winter, the maximum levels of bacteria were equal to 12,600 CFU/m3 in the kitchen, 50,400 CFU/m3 In the investigated houses, the highest value for air contamination with germs, at 37°C, was found in one of the apartments with one room (apartment B). Regarding the time of the day,

and 7,200 CFU/m3

air at different sample site. The amount of bacteria found in the outdoor

in the bathroom,

in the living room and 55,000

. In winter, the maximum values

as the background and

.

.

.

in the morning and 0 and 1,373 CFU/m3

with an average value equal to 1,476. CFU/m3

with an average value equal to 1,087 CFU/m3

.

. The fungi species identified were the same like in the present

in the bathroom, with the average values between 3,219

the highest level of bacteriological contamination was found in the evening, in the one room apartment—apartment B. Taking into account the time of the year, the indoor air contamina‐ tions were higher in the fall and winter, especially in the one room apartment—apartment B.

α Hemolytic streptococci/m3

β Hemolytic streptococci/m3

**Total number of bacteria at 37°/m3 air**

in Table 2.

508 Current Air Quality Issues

**Average values**

Ground floor

E = evening)

air were identified in the highest levels in the evening at the

*Staphylococcus aureus/m3*

 *air*

3108

in the kitchen and bathroom and 32,500

in the bathroom,

in the living room and 55,000

. In winter, the maximum values

as the background and

.

.

.

in the morning and 0 and 1,373 CFU/m3

with an average value equal to 1,476. CFU/m3

with an average value equal to 1,087 CFU/m3

.

and 7,200 CFU/m3

air at different sample site. The amount of bacteria found in the outdoor

in the kitchen, 50,400 CFU/m3

.

. The fungi species identified were the same like in the present

in the bathroom, with the average values between 3,219

**α Hemolytic streptococci/m3**

**air**

**β Hemolytic streptococci/m3**

**air**

air were identified at the ground floor only.

**Hemolytic staphylococci/m3**

**air**

M 2, 398.16 1, 323.2 78 47.3 86.5 0 E 3, 107.5 2, 217.85 90.08 164.76 75.65 0

**Table 2.** Levels of bacteriological air contamination in a shop with only a ground floor in Bucharest City (M = morning,

**Levels of mesophilic bacteria/m3 air average values** Levels of mesophilic bacteria ‐ average values

In a shop with only a ground floor, the levels of bacteriological contamination are presented

ground floor. In the evening, at the other floors, the levels were almost equal.

**air**

**Total number of staphylococci/m3**

Fig. 15. Levels of bacteriological contamination of air in shop with only a ground floor.

**Figure 15.** Levels of bacteriological contamination of air in shop with only a ground floor.

2398

contaminations were higher in the fall and winter, especially in the one room apartment—apartment B.

The ratio between staphylococci/germs at 37C is around 40% in the kitchen and over 50% in the living rooms. In a previous study [44], the levels of bacteriological and fungal pollution of indoor air were as follows:

in the living room with average values between 2,406 and 7,903 CFU/m3

*aureus*, and α hemolytic streptococci (Fig.15).

contamination of air where there were smokers present.

highest level in the apartment with two linked rooms.

the maximum level registered was equal to 1,820 CFU/m3

between 53 and 5,673 CFU/m3

air ranged from 10 to 310 CFU/m3

University in Addis Ababa [46] had the following results:

The levels of bacteria were between 367 and 2,595 CFU/m3

The levels of fungi were between 524 and 1,992 CFU/m3

In summer, the maximum levels of bacteria were equal to 57,500 CFU/m3

in the bathroom with average values between 4,308 CFU/m3

In winter, the maximum levels of bacteria were equal to 12,600 CFU/m3

in the living room with average values between 6,090 and 13,241 CFU/m3

The highest levels of staphylococci in air were, in the summer, between 26,125 CFU/m3

in the kitchen and 55,000 CFU/m3

Another study performed in Torun University in Poland [45] had the following results: The levels of air contamination with bacteria were between 7 and 3,313 CFU/m3

. The air pollution with fungi ranged between 0 and 893 CFU/m3

**8. Discussions**

CFU/m3

**8. Discussions**

CFU/m3

study.

and 6,300 CFU/m3

and 6,421 CFU/m3

were between 10,250 CFU/m3

.

In the shop with only a ground floor, the levels of air contamination registered higher values in the evening than in the morning measurements for mesophilic bacteria, total number of staphylococci, hemolytic staphylococci, *S.*

Morning Evening

In the shop with only a ground floor, the levels of air contamination registered higher values in the evening than in the morning measurements for mesophilic bacteria, total number of staphylococci, hemolytic staphylococci, *S. aureus*, and α hemolytic streptococci (Fig.15).

In the investigated houses, the highest value for air contamination with germs, at 37°C, was found in one of the apartments with one room (apartment B). Regarding the time of the day,

In the investigated houses, the highest value for air contamination with germs, at 37C, was found in one of the apartments with one room (apartment B). Regarding the time of the day, the highest level of bacteriological contamination was found in the evening, in the one room apartment—apartment B. Taking into account the time of the year, the indoor air

Considering the smoking habit of the inhabitants, apartments with one and two rooms had a higher bacteriological

Considering the type of the apartments, in apartments with more than one room, bacteriological contamination is at the

The air pollution with fungi was visualized as a layer on the Petri dish in the kitchen and bathroom, and in the living room

in the afternoon. The outdoor fungal background ranged between 217 and 3,750 CFU/m3

Another study that investigated the levels of biological contamination of indoor air in eight libraries of the Jimma

Considering the smoking habit of the inhabitants, apartments with one and two rooms had a higher bacteriological contamination of air where there were smokers present.

Considering the type of the apartments, in apartments with more than one room, bacteriolog‐ ical contamination is at the highest level in the apartment with two linked rooms.

The ratio between staphylococci/germs at 37°C is around 40% in the kitchen and over 50% in the living rooms.

In a previous study [44], the levels of bacteriological and fungal pollution of indoor air were as follows:

In summer, the maximum levels of bacteria were equal to 57, 500 CFU/m3 in the kitchen and bathroom and 32, 500 CFU/m3 in the living room with average values between 6, 090 and 13, 241 CFU/m3 .

In winter, the maximum levels of bacteria were equal to 12, 600 CFU/m3 in the kitchen, 50, 400 CFU/m3 in the bathroom, and 6, 300 CFU/m3 in the living room with average values between 2, 406 and 7, 903 CFU/m3 .

The highest levels of staphylococci in air were, in the summer, between 26, 125 CFU/m3 in the living room and 55, 000 CFU/m3 in the bathroom with average values between 4, 308 CFU/m3 and 7, 200 CFU/m3 . In winter, the maximum values were between 10, 250 CFU/m3 in the kitchen and 55, 000 CFU/m3 in the bathroom, with the average values between 3, 219 and 6, 421 CFU/m3 .

The air pollution with fungi was visualized as a layer on the Petri dish in the kitchen and bathroom, and in the living room the maximum level registered was equal to 1, 820 CFU/m3 . The fungi species identified were the same like in the present study.

Another study performed in Torun University in Poland [45] had the following results:

Another study that investigated the levels of biological contamination of indoor air in eight libraries of the Jimma University in Addis Ababa [46] had the following results:

The microbiological quality of air in the investigated rooms in the Bucharest school as well as in the Giurgiu school changed significantly during the school day. The highest levels were found around 11:00 a.m. in classrooms with large numbers of persons inside. Most of the levels identified in the study were under the limit of 1, 000 CFU/m3 , with the exceptions of one measurement in the school in Bucharest and four measurements in the school in Giurgiu City. Also, the presence of *Streptococcus* beta hemolytic and coliform bacteria in classrooms air represents a threat to the health of children. This threat appears to be greater in the school in Giurgiu City.

The fungi levels identified in offices and archives were over the limit accepted in Romania. Isolated fungi species include *Cladosporium*, *A. flavus*, *A. niger*, and *Penicillium* sp.

In the case of the shop with only a ground floor and the supermarket, it is obvious that the size of the shop, the numbers of floors, and the numbers of departments in the shop can induce a high bacteriological contamination of air due to the high number of people visiting the shops. The air contamination in the supermarket was found to be higher at the ground floor and at the third and fourth floors. The high level of bacteriological contamination at the ground floor is probably due to the large number of visitors, while at the third and fourth floor it is probably due to the accumulation of bacteria coming from the first and second floors. In the shop with only a ground floor, the levels of air contamination in the evening were lower than in the morning. It is probable that this type of shop has better ventilation. High levels of staphylococci and the presence of *Streptococcus* in air can be dangerous for human health, and it is necessary to have a very strong disinfection action.

## **9. Conclusions**

From this study, several conclusions can be drawn:


*Edited by Mohamed Samer*

This book provides useful information about Urban Agriculture, which includes the production of crops in small to large lots, vertical production on walls, windows (window farms), rooftops (green roofs), urban gardens, farmer's markets, economic models of urban gardening, peri-urban agricultural systems, and spatial planning and evolution of the land uses. Additionally, this book elucidates further agricultural technologies, such as the aquaculture systems.

ISBN 978-953-51-2403-0

Urban Agriculture

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## Urban Agriculture

*Edited by Mohamed Samer*