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Industrial pollution in Bangladesh has posed a serious threat to human health, economic activity, and the environment. By emphasizing industries that produce major pollutants, substantial improvements can be made to pollution mitigation measures. In countries where primary pollution data is not readily available, the Industrial Pollution Projection System (IPPS) could be used to calculate the pollution load utilizing total industrial output or employment data. IPPS data, which was designed for developed countries like the USA, had been used directly for other countries without any normalization in previously reported studies. The main purpose of this study is to modify the current IPPS approach for any other country by incorporating specific correction factor for a specific country. In this study, a specific correction factor for Bangladesh was determined, taking into account the country's major polluting industries, and used to estimate the pollution scenario for the year 2020. The accuracy of the specific pollution intensities was also evaluated by comparing the data obtained using both gross output and employee number. According to this study, the top three air-polluting industries are structural clay products, cement-lime-plaster industry, and iron and steel industry. Similarly, for water pollution, the food industry, paper and paper product industry, and textile industry are the largest pollutant contributors. The detailed pollution load matrix in terms of air and water pollution is also developed, and can be used to predict both short-term and long-term scenarios of industrial pollution in Bangladesh, which eventually will assist the policy makers to adopt appropriate pollution management approach. Moreover, the methods developed in this study will help to tailor the IPPS data for any country and increase the accuracy of the pollution load. [ABSTRACT FROM AUTHOR]

The deposition of trace elements around a pulp and paper industry in Morelia, Mexico, was evaluated using two lichen species as biomonitors. Samples of the foliose lichen Flavopunctelia praesignis and the fruticose lichen Usnea ceratina were collected in two remote areas and transplanted at different distances and directions from the pollution source. Lichen samples were exposed for 4 months (1) around the industrial area and (2) in their native habitats (control sites). We investigated the bioaccumulation of 11 trace elements in lichen thalli, and we compared the response of the two lichen species. To identify possible common sources, we evaluated the relationships between trace elements by correlations and cluster analyses. Our results showed that Cd was a good tracer for air pollution from the pulp and paper mills. In samples of Usnea ceratina exposed around the industrial area, Cd was significantly higher than in the remote area. Within the study area, trace element contents increase with the distance from the source, and they showed high depositions in the direction of prevailing winds. Moreover, we were able to detect groups of elements with similar behavior and common origins. Our results indicated that Flavopunctelia praesignis showed a higher capacity to accumulate trace elements than Usnea ceratina. [ABSTRACT FROM AUTHOR]

With the rapid development of the economy, the expressway has been used as a main mode of transportation due to its function to meet traffic demand of people and thus has been given full attention. But, at the same time, it has gradually become the main cause of pollution of traffic environment. To clarify the degree of pollution caused by expressway vehicle and improve the expressway pollution diagnosis system, upon the notion of low-carbon transportation, this paper divides expressway environmental pollution into four types: air pollution, photochemical smog pollution, noise pollution, and vibration pollution, and analyzes each of them, respectively. Then, a comprehensive diagnosis model of environmental pollution caused by running vehicles will be built. This paper monitors the pollution intensity on different spots on the expressway to obtain the single-vehicle factors of various pollutants of the motor vehicles. Combined with the geographic information system, this puts forward the diagnosis methods in terms of the environmental "air pollution," "photochemical smog pollution," "noise pollution" and "vibration pollution" caused by the expressway vehicles, respectively, and further establishes a diagnosis model of vehicle pollution corresponding to the characteristics of the expressway. The result of the case study on the actual monitoring data of six expressways in Jiangsu Province shows that the pollution diagnosis values of six expressways are all between (0.4, 0.6] which symbolizes "slight pollution." The research results can provide technical support for monitoring of environmental pollution caused by expressway more comprehensively and reasonably, and provide data support for formulating effective control strategies. [ABSTRACT FROM AUTHOR]

With the rapid development of the economy, the expressway has been used as a main mode of transportation due to its function to meet traffic demand of people and thus has been given full attention. But, at the same time, it has gradually become the main cause of pollution of traffic environment. To clarify the degree of pollution caused by expressway vehicle and improve the expressway pollution diagnosis system, upon the notion of low-carbon transportation, this paper divides expressway environmental pollution into four types: air pollution, photochemical smog pollution, noise pollution, and vibration pollution, and analyzes each of them, respectively. Then, a comprehensive diagnosis model of environmental pollution caused by running vehicles will be built. This paper monitors the pollution intensity on different spots on the expressway to obtain the single-vehicle factors of various pollutants of the motor vehicles. Combined with the geographic information system, this puts forward the diagnosis methods in terms of the environmental "air pollution," "photochemical smog pollution," "noise pollution" and "vibration pollution" caused by the expressway vehicles, respectively, and further establishes a diagnosis model of vehicle pollution corresponding to the characteristics of the expressway. The result of the case study on the actual monitoring data of six expressways in Jiangsu Province shows that the pollution diagnosis values of six expressways are all between (0.4, 0.6] which symbolizes "slight pollution." The research results can provide technical support for monitoring of environmental pollution caused by expressway more comprehensively and reasonably, and provide data support for formulating effective control strategies. [ABSTRACT FROM AUTHOR]

Diwali has become an occasion of air and noise pollution, and the release of particulate matter and toxic gases has chronic and acute effects on people and their environment. Thus, an air quality assessment study was done by CSIR-CSMCRI covering the pre-to-post Diwali 2021 period (5 days) in the three locations (traffic, residential, and control) of Bhavnagar. The average 24-h concentration of PM10 (380 µg/m3), PM2.5 (182.2 µg/m3), and SPM (403 µg/m3) was significantly higher during Diwali, exceeding the National Ambient Air Quality Standards (NAAQS). The concentrations of SO2 and NO2 were 121.8 µg/m3 and 102.1 µg/m3. Metals like Zn, Al, Pb, and Mn were found in higher concentrations during the study. The air quality index (AQI) was maximum on Diwali, resembling very poor air quality. More elements and oxides were detected in PM2.5 (S, Al, Mg, Ba, and Zn and their oxides) than in PM10 (Fe and S) through WDXRF. Water-soluble anions like SO42−, Cl−, and NO3− were observed during the study, with a higher SO42− (64%) on Diwali. The PM10 morphology and mapping of elements were done using SEM–EDX. Emerging contaminants, specifically phthalate groups, were detected through GCMS. The enrichment factor (EF) showed Zn and Pb originating from anthropogenic activities. The air quality data was validated using a variance test, least significance difference (LSD), correlation, and principal component analysis (PCA). This paper is the first to highlight the air quality assessment during Diwali for a western coastal place in India. It is time to implement regulations on burning firecrackers for pollution reduction, aiming to achieve a sustainable atmosphere. [ABSTRACT FROM AUTHOR]

Air pollution has a significant global impact on natural resources and public health. Accurate prediction of air pollution is crucial for effective prevention and control measures. However, due to regional variations, different cities may have varying primary pollutants, posing new challenges for accurate prediction. In this paper, we propose a novel method called FP-RF, which integrates clustering algorithms to categorize multiple cities according to their air quality index values. Subsequently, we apply functional principal component analysis to extract the primary components of air pollution within each cluster. Furthermore, an enhanced random forest algorithm is utilized to predict air quality grades for each city. We conduct experimental evaluations using authentic historical data from Anhui Province spanning from 2018 to 2023. The results unequivocally establish the effectiveness of our model, with an average accuracy rate of 98.6% in forecasting six pollution grades and 96.04% accuracy in predicting 16 prefecture-level cities, surpassing performance compared to other baseline models. [ABSTRACT FROM AUTHOR]

The development of contemporary artificial intelligence (AI) methods such as artificial neural networks (ANNs) has given researchers around the world new opportunities to address climate change and air quality issues. The small size, low cost, and low power consumption of sensors can facilitate obtaining the values of polluting gases in the atmosphere. However, several problems with using air pollution technique relate to various effects such as sensing accuracy, sensor drifts, and sluggish reactions to changes in pollution levels. Recently, machine learning has made it feasible to build a more intelligent, context-aware system that can anticipate events and monitor present conditions. This paper focuses on the use of environment sensors for detecting air pollution based on several types of wireless protocols, including Wi-Fi, Bluetooth, ZigBee, LoRa, Global Positioning System (GPS), and 4G/5G. Furthermore, it classifies previous published articles on the topic according to the wireless protocol and compared in terms of several performance metrics such as the adopted air pollution sensors, hardware platform, adopted algorithm, power consumption or power savings, and sensing accuracy. In addition, this work highlights the challenges and limitations facing drones during their mission for detecting air pollution. As a result, we suggest to build and implement at base station an intelligent system based on backpropagation (BP) neural networks, which provides flexibility to track and predict the true values of polluting gases in the atmosphere to overcome the above problems. Finally, this work addresses the advantages of using drones in the air pollution field. [ABSTRACT FROM AUTHOR]

This paper is aimed at developing an air quality monitoring system using machine learning (ML), Internet of Things (IoT), and other elements to predict the level of particulate matter and gases in the air based on the air quality index (AQI). It is an air quality assessor and therefore a means of achieving the Sustainable Development Goals (SDGs), in particular, SDG 3.9 (substantial reduction of the health impacts of hazardous substances) and SDG 11.6 (reduction of negative impacts on cities and populations). AQI quantifies and informs the public about air pollutants and their adverse effects on public health. The proposed air quality monitoring device is low-cost and operates in real-time. It consists of a hardware unit that detects various pollutants to assess air quality as well as other airborne particles such as carbon dioxide (CO2), methane (CH4), volatile organic compounds (VOCs), nitrogen dioxide (NO2), carbon monoxide (CO), and particulate matter with an aerodynamic diameter of 2.5 microns or less (PM2.5). To predict air quality, the device was deployed from November 1, 2022, to February 4, 2023, in certain bauxite-rich areas of Adamawa and certain volcanic sites in western Cameroon. Therefore, machine learning algorithm models, namely, multiple linear regression (MLR), support vector regression (SVR), random forest regression (RFR), XGBoost (XGB), and K-nearest neighbors (KNN) were applied to analyze the collected concentrations and predict the future state of air quality. The performance of these models was evaluated using mean absolute error (MAE), coefficient of determination (R-square), and root mean square error (RMSE). The obtained data in this study show that these pollutants are present in selected localities albeit to different extents. Moreover, the AQI values obtained range from 10 to 530, with a mean of 132.380 ± 63.705, corresponding to moderate air quality state but may induce an adverse effect on sensitive members of the population. This study revealed that XGB regression performed better in air quality forecasting with the highest R-squared (test score of 0.9991 and train score of 0.9999) and lowest RMSE (test score of 1.5748 and train score of 0. 0073) and MAE (test score of 0.0872 and train score of 0.0020), while the KNN model had the worst prediction (lowest R-squared and highest RMSE and MAE). This embryonic work is a prototype for projects in Cameroon as measurements are underway for a national spread over a longer period of time. [ABSTRACT FROM AUTHOR]

Air pollution has been integrated into global challenges over the last few years due to its negative impact on the health of human beings, increasing socio-economic risks, and its contribution to climate change. This study attempts to evaluate the current status of Iran's air pollution with regard to the sources of emissions, control policies, and the health and climate consequences that have resulted through available data from monitoring stations reported in the literature, official documents, and previously published papers. Many large cities in Iran surpass the permissible concentration of air pollutants, particularly particulate matter, sulfur dioxide, black carbon, and ozone. Although regulations and policies are in place and enormous efforts are being made to address air pollution issues in the country, implementation and enforcement are not as effective as they could be. The significant challenges may be regarded as the inefficiency of regulation and supervision systems, the lack of air quality monitoring systems and technology, particularly in industrial cities rather than Tehran, and the lack of continual feedback and investigations on the efficiency of regulation. Providing such an up-to-date report can bring opportunities for international collaboration, which is essential in addressing air pollution worldwide. We suggest that a way forward could be more focused on conducting systematic reviews using scientometric methods to show an accurate picture and trend in air pollution and its association in Iran, implementing an integrated approach for both climate change and air pollution issues, collaborating with international counterparts to share knowledge, tools, and techniques. [ABSTRACT FROM AUTHOR]

High-resolution mapping of PM2.5 concentration over Tehran city is challenging because of the complicated behavior of numerous sources of pollution and the insufficient number of ground air quality monitoring stations. Alternatively, high-resolution satellite Aerosol Optical Depth (AOD) data can be employed for high-resolution mapping of PM2.5. For this purpose, different data-driven methods have been used in the literature. Recently, deep learning methods have demonstrated their ability to estimate PM2.5 from AOD data. However, these methods have several weaknesses in solving the problem of estimating PM2.5 from satellite AOD data. In this paper, the potential of the deep ensemble forest method for estimating the PM2.5 concentration from AOD data was evaluated. The results showed that the deep ensemble forest method with R 2 = 0.74 gives a higher accuracy of PM2.5 estimation than deep learning methods ( R 2 = 0.67 ) as well as classic data-driven methods such as random forest ( R 2 = 0.68 ). Additionally, the estimated values of PM2.5 using the deep ensemble forest algorithm were used along with ground data to generate a high-resolution map of PM2.5. Evaluation of produced PM2.5 map revealed the good performance of the deep ensemble forest for modeling the variation of PM2.5 in the city of Tehran. [ABSTRACT FROM AUTHOR]

The objective of this paper is to provide an estimation of air emissions (CO2, NOx, SOx and PM) released by port assistant vessels at port level. The methodology is based on the "full bottom-up" approach and starts by assessing the fuel consumed by each tug ship during its individual port exercises (movements during docking and undocking of merchant vessels). The scenario selected for the analysis and measurements is one of the most significant Port of the Mediterranean Sea, where seven auxiliary vessels were monitored for 407 calls. The analysis also gathers real-time data from the Automatic ship Identification System (AIS), tug ship particulars from IHS sea-web database (www.maritime.ihs.com) and emission factors established by the International Maritime Organization (IMO). The research findings show that the key indicators are inventory emissions per dock, types of towed vessels and docking and undocking manoeuvres. This paper also presents an action protocol for the assessment of the inventory of emissions produced by the main engines of tug ships operating inside ports, which can be extrapolated to other ports operating with tug ships of the same technical characteristics. Evaluating, therefore, the amounts emitted of nitrogen oxides, sulphur oxides, carbon dioxide and particulate matter. [ABSTRACT FROM AUTHOR]

This research paper examines the exposure to particulate matter (PM) and its deposition on the human respiratory tract (HRT) in 12 critical urban zones — institutional zone, commercial zone, construction zone, hospital zone, landfill zone, industrial zone, residential zone, high-traffic zone, main roads, medium-traffic zone secondary roads, low-traffic zone, coastal zone, and environmentally sensitive zone. This study measured the size-segregated PM concentrations using a Grimm aerosol spectrometer. The multiple-path particle dosimetry model assesses particles' total and regional deposition mass rates for different urban zones. A stochastic model of the 60th percentile is used to illustrate the deposition of PM in the human lung. The deposition rate of PM in the HRT is examined for the different urban zones from different emission sources. The analysis shows that the PM concentration in zone V (dumpsite zone) is at an elevated level (i.e., PM10 = 570.4 µg/m3, PM2.5 = 128.3 µg/m3, and PM1 = 28.1 µg/m3) and lowest at zone XII (eco-sensitive zone) (i.e., PM10 = 25.1 µg/m3, PM2.5 = 1 6.9 µg/m3, and PM1 = 14.8 µg/m3). Further, dumpsite, commercial, and eco-sensitive zones are identified to be critical zones that influence higher deposition in the tracheobronchial and pulmonary regions. The investigation concludes that local turbulence and emission source significantly impacts air quality and deposition of PM at HRT. In addition, as the PM diameter decreases, the acidity of PM increases, and it can penetrate deep into the lower airways. Since this can have profound consequences, it is imperative to better understand the deposition of PM across various urban zones. [ABSTRACT FROM AUTHOR]

Owing to the rise in population, lifestyle changes, high traffic rates in urban areas and environmental pollution, respiratory diseases have become much more prevalent on both regional and urban scales. Respiratory diseases affect over 300 million people worldwide and are thus among the major threats to humans' general well-being. The identification of underlying factors and the specification of accompanying risk areas for the temporal exacerbation of respiratory diseases are effective steps in managing the damage caused by such disorders. Here, we demonstrate a strategy for modelling the risk zone of respiratory diseases temporally, using a location-based social network (LBSN) and an artificial neural network (ANN). The main contribution of this paper is to consider the environmental and infrastructural factors and identify their relationships with the geographical locations of respiratory attacks. The study also utilizes Telegram, which is the most popular and conventional social media platform, in order to observe temporal changes in the location of respiratory attacks in Iran, in the form of a developed Telegram bot known as @respiratoryassociation. The relations between the factors behind and the location of respiratory attacks are determined using a multilayer perceptron (MLP) ANN. All the required data have been collected on a daily basis over a 5-year period from December 2013 to December 2018 in Tehran, Iran. The results indicated air pollution, especially pollution from carbon monoxide (CO) and suspended particulate matter (PM) as the most decisive factors. Following air pollution, the amount of exposure to the polluted area was determined as the second most decisive factor, which in turn increased as a result of escalations in traffic jams. Land use was determined as the third most decisive factor. Furthermore, the results revealed that the ANN performed satisfactorily, implying that the model can be used to examine the spatio-temporal behaviour of the time series of respiratory diseases with respect to environmental and infrastructural factors. [ABSTRACT FROM AUTHOR]

This paper analyzes the building process of the main greenhouse gas (GHG) emissions (CO2, CH4 and N2O) inventory from the energy sector in Palestine. The paper includes determination tools, i.e., emission factors, to estimate the amounts of national GHG emissions from sub-sectors of energy including energy industries, manufacturing industries and construction, transport and other sectors (households, agriculture and commerce and public services). The results show that the total amount of national GHG emissions from the energy sector in 2016 was 4131 thousand metric tons of CO2-equivalent (TtCO2e), which represented 0.011% of the total global GHG emissions. The average value of GHG emissions per capita from the energy sector was 0.86 tCO2e in Palestine, and its gross domestic product was estimated at 3212 $/ton of CO2e. The estimated amounts of CO2, CH4 and N2O emission from the energy sector were 4022, 49 and 60 TtCO2e, respectively. The transport and household sub-sectors dominated the national GHG emissions from the entire energy sector by 58 and 32%, respectively. In general, fuels including diesel, gasoline, wood and charcoal and liquefied petroleum gas made most of the total amount of the national GHG emissions from the energy sector at 50, 18, 18 and 12%, respectively. Finally, the mitigation actions included in the first nationally determined contribution of Palestine and recommendations to help lower the national GHG emissions from the Palestinian energy sector are provided. [ABSTRACT FROM AUTHOR]

The application of restoration plans for the Iraqi marshlands is encountering significant challenges due to water scarcity and the impacts of climate change. This paper assesses the impact of water scarcity on the possibility of continuing the application of restoration and sustainable management plans for the main marshlands in Iraq. This assessment was conducted based on the available data and expected situation of available water resources under climate change conditions until the year 2035. Additionally, a satellite image–based index model was prepared and applied for the period 2009–2020 to obtain the spatiotemporal distribution of the restored marshlands. The results show that the shortage in water resources and insufficient inundation rates prevented the adequate application of the restoration plans. Also, applying the scenarios of distributing the deficit equally over all water demand sectors (S1) and according to the percentage of demand for each sector (S2) shows that the expected deficit in available water for the three marshes by the years 2025 and 2035 will be approximately 25% and 32% for S1 and 9% for S2. Consequently, the considered marshes are expected to lose approximately 20 to 33% of their eligible restoration areas. Accordingly, looking for suitable alternatives to support the water resources of these marshes became a very urgent matter and/or recourse to reduce the areas targeted by inundation and being satisfied with the areas that can be sustainable and maintain the current status of the rest of the regions as an emerging ecosystem characterized by lands that are inundated every few years. Accordingly, steps must be urged to develop plans and programs to maintain the sustainability of these emerging ecosystems within the frameworks of climate change and the conditions of scarcity of water resources and water and air pollution to ensure that they are not lost in the future. [ABSTRACT FROM AUTHOR]

The aim of this study was to investigate the effect of changes in meteorological parameters and fuel composition on the emission rate of air pollutants in the vehicle fleet of the Tehran Metropolis. The results of this study can be used in management decisions to reduce the emission of air pollutants. In this paper, based on the international vehicle emission model and using mathematical equations, the effects of changing meteorological parameters and fuel composition on the emission of pollutants were modeled. The emission rates of CO, VOCs, and NOx pollutants were the most sensitive to the changes in meteorological parameters, respectively. Among all parameters studied in this research, the changes in sulfur level had the greatest effect on the emission of pollutants from the vehicle fleet of Tehran Metropolis. If the fuel was replaced with Euro 5 standard instead of Euro 3, the emission rates of CO, VOCs, NOx, PM, and SOx pollutants from the vehicle fleet of Tehran Metropolis would be reduced by 9%, 6%, 5%, 14%, and 90%, respectively. Managing and reducing the sources of production and emission of air pollution is one of the best ways to reduce the air pollution. In general, since the emission of pollutants from the fleet of Tehran Metropolis in the cold seasons of the year is greater than during hot seasons and the problem of air pollution is exacerbated by air stability, using Euro 5 fuel in cold seasons is one of the efficient ways to reduce the air pollution. [ABSTRACT FROM AUTHOR]

Taking detailed rules for implementing of the plan on atmospheric pollution prevention and control in Beijing-Tianjin-Hebei region and surrounding areas promulgated and implemented in 2013 as the starting point, taking air pollution as the research object, using the propensity score matching method and difference-in-differences method to answer whether coordinated development of Beijing-Tianjin-Hebei is conducive to resolving the problem of air pollution. Using panel data from 251 cities in China 2003–2016, this paper discusses the environmental effects and mechanisms of coordinated urban cluster development. The results showed that the coordinated development of Beijing-Tianjin-Hebei had a significant and continuous effect on the convergence of PM2.5 to low levels. The results of further mechanism identification show that the policy of atmospheric joint defense rules in the process of Beijing-Tianjin-Hebei coordinated development mainly promotes the convergence of PM2.5 through population and economic scale effect, structural effect and technical effect, and promotes the convergence of PM2.5 in the low direction by reducing the economic growth scale of the city itself. It is suggested to further promote the formation and development of Beijing-Tianjin-Hebei integration, strengthen cooperation among local governments in environmental governance e across regions, and promote the green growth and coordinated development of urban clusters. [ABSTRACT FROM AUTHOR]

This paper provides an overview of the trend of generation, composition, and management of municipal solid waste, and estimates the carbon emissions arising from municipal solid waste management in Beijing. The correlation analysis conducted shows that the generation of municipal solid waste in Beijing has been growing steadily, showing high correlations ( r > 0.9) to the total GDP, per capita income, and the population. Food waste showed an increasing trend since 1990. Compared with the results of an investigation in 1990, ash and woodchips content in 2003 declined from 56% to 17%, while the percentage of paper and plastic increased from 10% to 29% over the same period. The calorific value of the municipal waste also increased, from 2,686 kJ/kg in 1990 to 4,667 kJ/kg in 2003, indicating that the waste is suitable for incineration. Currently, the source separation ratio of municipal waste is approximately 15%. About 94% of all the collected solid waste goes to the landfill while 4% is composted and 2% is incinerated. A moderate garbage collection fee is applied to both permanent and temporary residents in Beijing, but the willingness to pay for solid waste collection and treatment is still low. Under current treatment mode, the total amounts of carbon emission from waste disposal sites and incineration increased with the increase of municipal solid waste, from 29.8 Gg in 1990 to 84.5 Gg in 2003, including 83.3 Gg of CH4 and 22.0 Gg of CO2. The data availability and methodological challenges in monitoring the quantity and characteristics of municipal solid waste are discussed. [ABSTRACT FROM AUTHOR]

The paper presents a study into air borne respirable dust (ARD) concentration at various workplaces in two open cast chromite mines of Sukinda chromite belt, India. One of these mines is mechanised and the other one is semi-mechanised. The study has been conducted in three phases and ARD concentration has been measured at various workplaces in both the mines. Apart from the determination of ARD concentration, the dust samples collected on filter papers were analysed for hexa-valent chromium by colorimetric method and Cr(VI) direct absorption measurement. In addition, the particle size ranges in ARD have been determined by using cascade impactor fitted onto high volume samplers and laser guided particle size analyser. The paper finally presents a comparison between ARD concentration vis-à-vis the presence of hexa-valent chromium concentration in collected samples of both mechanised and semi-mechanised mines. [ABSTRACT FROM AUTHOR]

The Sichuan-Chongqing region is the leader and growth pole of economic development in western China. With the rapid development of economy and unique geographical environment, high concentration of sulfur dioxide air pollution has existed for a long time in Sichuan-Chongqing area. Based on 10 years of remote sensing data, this paper studies the temporal and spatial distribution characteristics, stability, and influencing factors of sulfur dioxide in this area. Based on potential sources, the impact of surrounding areas on sulfur dioxide in Sichuan and Chongqing is analyzed. The results shows that the spatial distribution of sulfur dioxide in the Sichuan-Chongqing region is higher in the southeast and lower in the west. The Midwest region has low fluctuation and good stability. The time distribution shows obvious seasonal regularity. The concentration of sulfur dioxide is affected by socio-economic factors and natural factors. In this study, it is found that the distribution of sulfur dioxide is closely related to PM2.5, which provides an important reference for the comprehensive management of air pollution. The OMI data effectively reflects the distribution and change of atmospheric sulfur dioxide in the Sichuan-Chongqing region, and provides certain ideas for air pollution control in the Sichuan-Chongqing region and other regions in China. [ABSTRACT FROM AUTHOR]

Middle East and North African (MENA) countries over the decades are experiencing rapid industrial and infrastructural growth combined with being the global hub of oil and gas industries. These economic transformations are associated with release of air pollutants including urban air toxics (UAT) through industrial, traffic, and constructional activities into ambient urban environments. UAT concentrations levels may exacerbate in most MENA countries considering high number of vehicular traffic populations and petrochemical industries which are one of the main sources of this pollutant. Therefore, the main objective of the study is to review major findings of UAT levels in urban areas across thirteen (13) MENA countries. The study characterizes various measured UAT, assesses their concentrations in ambient environment, and identifies their major sources of emissions by reviewing more than 100 relevant UAT papers across the selected MENA countries. It was found that benzene, heavy metals, formaldehyde, and dioxin-like compounds are the most reported UAT. The study concluded that road traffic, fuel stations, and petrochemical industries were identified as the main sources of ambient UAT levels. It was further reported that most of the studies were based on short-term ambient environment with limited studies in indoor environments. Therefore, it is highly recommended that future research should focus on innovative health impact assessment and epidemiological studies from exposure to UAT levels. Also embarking on sustainable mitigation approaches through urban greenery, eco-industrial estates infrastructural developments, and renewable energy shares will reduce UAT levels and improve human health. [ABSTRACT FROM AUTHOR]

In this paper, a 2-D turbulent closure model, based on the pollutant mass conservation equation, is adopted to estimate the local and background pollutants in the predominant wind direction for the stable atmosphere during winter mornings. The background concentration of pollutants can severely affect the regional pollution level, and its monitoring is a challenging task. Here, the turbulent closure model is employed across three cities in India, viz., Patiala, Delhi, and Agra, to estimate SO2 and NOx concentration along the predominant wind direction to demonstrate the potential of numerical models. The direction of the prevailing wind in this area during January 2003 was NNW (330°). Patiala is followed by Delhi and then Agra in the predominant wind direction. The sensitivity analysis of surface temperature on pollutant concentration reveals that concentration would increase by its square as temperature dips. So, during low or no horizontal wind, pollution episodes will be inevitable. Thus, the pollution hotspots are also identified in these three cities. Delhi had a high pollution load. So, the impact of local pollution in Delhi, through dispersion, was found significant in Agra. NOx hot spots (exceed the 30 μg/m3 limit) are found all across Delhi, except IGI Airport and two other locations. However, no SO2 hotspot (exceed the 60 μg/m3 limit) is found in Delhi. The proposed model output is verified with the WRF-CFD model results. Compared to the WRF-CFD model, the proposed model has overestimated NOx and SO2 concentration maximum by 14.4% and 23.5%, respectively. The overestimation occurred primarily due to ignoring atmospheric chemical reactions (e.g., acid condensation, etc.) for which the atmospheric factors were not so conducive. [ABSTRACT FROM AUTHOR]

Fine particulate matter (PM2.5) has become a significant issue of ecological environment. However, few studies have explored the vertical distribution of PM2.5 in cities. The objectives of this paper are to reveal the vertical distribution regular pattern of PM2.5 over urban underlying surfaces near the ground with a hexacopter-type unmanned aerial vehicle (UAV) in winter. Results showed that the maximum vertical gradient of PM2.5 near the ground was typically the greatest in the morning as the stable atmospheric conditions. Moreover, regression model illustrated that relative humidity had the greatest impact on the vertical profile of PM2.5 compared to air temperature and altitude as hygroscopic of PM2.5 aerosols. Curve model shown that vertical profile of PM2.5 over the surfaces of water and green space first increased slowly and then declined, besides, the highest concentration inflection of PM2.5 above the water body (23.7 m) is higher than the green space (14.3 m). Thus, suggesting residents living vertical of 10–30 m from the ground around large water bodies and green spaces should not open windows for ventilation in the morning. Therefore, this study provides insights into the vertical distributions of PM2.5 over different underlying surfaces and should be of reference value to urban planners for designing urban spaces to optimize atmosphere environment to provide a healthy living environment. [ABSTRACT FROM AUTHOR]

In this paper, we described generation and performances of feedforward neural network models that could be used for a day ahead predictions of the daily maximum 1-h ozone concentration (1hO3) and 8-h average ozone concentration (8hO3) at one traffic and one background station in the urban area of Novi Sad, Serbia. The six meteorological variables for the day preceding the forecast and forecast day, ozone concentrations in the day preceding the forecast, the number of the day of the year, and the number of the weekday for which ozone prediction was performed were utilized as inputs. The three-layer perceptron neural network models with the best performance were chosen by testing with different numbers of neurons in the hidden layer and different activation functions. The mean bias error, mean absolute error, root mean squared error, correlation coefficient, and index of agreement or Willmott's Index for the validation data for 1hO3 forecasting were 0.005 μg m−3, 12.149 μg m−3, 15.926 μg m−3, 0.988, and 0.950, respectively, for the traffic station (Dnevnik), and − 0.565 μg m−3, 10.101 μg m−3, 12.962 μg m−3, 0.911, and 0.953, respectively, for the background station (Liman). For 8hO3 forecasting, statistical indicators were − 1.126 μg m−3, 10.614 μg m−3, 12.962 μg m−3, 0.910, and 0.948 respectively for the station Dnevnik and − 0.001 μg m−3, 8.574 μg m−3, 10.741 μg m−3, 0.936, and 0.966, respectively, for the station Liman. According to the Kolmogorov–Smirnov test, there is no significant difference between measured and predicted data. Models showed a good performance in forecasting days with the high values over a certain threshold. [ABSTRACT FROM AUTHOR]

This paper presents a method for constructing a membership function (MF) for the fuzzy sets that expert systems deal with. This paper introduces a Bezier curve-based mechanism for constructing MFs of convex normal fuzzy sets. The mechanism can fit any given data set with a minimum level of discrepancy. In the absence of data, the mechanism can be intuitively manipulated by the user to construct MFs with the desired shape. MFs have been developed using the proposed mechanism for urban vehicular exhaust emission modeling. It has been observed that all meteorological and vehicular parameters have either S-shaped MFs or Z-shaped MFs. Gaussian MF has been mostly applied for modeling air quality. The present study explored the application of fuzzy MF to analyze air pollution data from vehicular emission. The study reveals that S-shaped and Z-shaped MF can be used in addition to Gaussian MF. [ABSTRACT FROM AUTHOR]

This paper presents the externalities of sulfur dioxide (SO2) in the Taichung metropolitan, Taiwan. The human effects of SO2 from stationary sources are a key element for local air quality management. A methodology to evaluate those effects in monetary term had been adapted and applied. This method was modified from the EU-based impact pathway approach link to the skill of geographic information systems. The studied area was divided into 66 districts and the quantity of SO2 was allocated to each district. Then, the ambient concentration of SO2 was estimated by using the ordinary kriging method. A risk analysis was employed to characterize the health impacts caused by SO2. Finally, a monetary transfer approach based on the European data was conducted to estimate local damage costs per ton of SO2. Health impacts, especially acute mortality, dominate in term of costs. The calculation showed that damage costs ranged from 0.56 to 7.38 USD/kg SO2 based on the 2002 emission data. The results could be used in the internalization of SO2 externalities and management of the stationary sources in the studied area. This paper also concludes that the externalities caused by stationary-related SO2 emissions are some 28.5 million US dollars in Taichung metropolitan (Taiwan) in 2002. [ABSTRACT FROM AUTHOR]

This paper introduces the five papers that follow, all of which were originally presented at a workshop titled “Monitoring the Environment: Scales, Methods, and Systems in Historical Perspective.” The workshop, sponsored by the Society for the History of Technology and the American Society for Environmental History, examined past efforts to develop tools, methods, and systems for measuring or monitoring some aspect of the physical environment. Four of the papers included here focus on various aspects of air quality monitoring; the fifth has to do with monitoring the earth from space. Despite differences in time period and approach, each article examines how specific tools and methods - and the motivations for developing those tools and methods - evolved. Among other things, these papers make clear that systems for monitoring various aspects of the physical environment are shaped by a variety of stakeholders and suggest that efforts to construct such systems should not be viewed as a purely technical task. [ABSTRACT FROM AUTHOR]

This paper describes one example of how the UK National Air Quality Strategy (NAQS) is implemented in a local urban environment. The paper reviews the beginning of this process, by examining the review and assessment procedures of the NAQS in the London Borough of Barnet. By the application of available UK tools of local air quality management (LAQM), the process began through analysis of the levels of local emissions and progressed onto modelling of current and future air quality. A map showing combined emission hotspot areas for the Borough indicated and higher emission rates occur in the south of the Borough and along the major transport corridors, as road sources dominate emissions. Dispersion modelling studies were also conducted for this purpose, using the screening models GRAM, PGRAM and ADMS Urban for an in-depth assessment. These analyses found that some local point sources and the majority of Borough roads with over 20,000 vehicles per day produced exceedances of the future objectives for air quality for some pollutants. Recommendations for the progression of LAQM in the Borough are made and include the update and expansion of the emissions information held for use in future modelling studies. The paper demonstrates the experience of implementing the Strategy, using the tools and procedures available for this purpose, in a local urban environment that is similar to many in the UK. [ABSTRACT FROM AUTHOR]

The main goal of sustainable development is to engage the public in setting the groundwork for developing profiles based on carrying capacity assessments. Since industrial projects are located in traditional, non-industrial zones, the broad human resource development program includes environmental research, education, and training to build technical and practical skills in the country-based and scientific statistics system data gathering. It is noteworthy that the examinations were conducted sporadically and that the research did not correspond to the pollution level in Bangladesh's waterways. Therefore, it is essential to conduct a methodical examination that may offer a complete picture of river pollution so that appropriate preventative actions can be adopted to safeguard against pollution threats. Bangladesh has many environmental issues, including dirty air from various vehicles, unhealthy water, accumulating urban waste, untreated sewage, interior air pollution from wood smoke, and usage of fad-driven contemporary materials in homes. Each of them contributes more to the destruction of the environment. With the current trend of population growth followed by the construction of companies to suit their wants, such a situation may worsen. Hazardous gases, dust particles, or liquid effluents are the waste products emitted from industrial sources. These discharges are full of harmful chemicals that pollute aquatic ecosystems, disrupt the local biota, and eventually harm living things and associated flora and animals. As a result, studies were conducted, including the physicochemical characterization of sugar refineries, distilleries, and other rivers that receive such effluents. These characteristics of subsurface water were also taken into consideration throughout the inquiry. This research will be anticipated to offer proper preventive methods for preserving the purity of the Bangladeshi rivers. [ABSTRACT FROM AUTHOR]

Novel machine learning models (MLMs) using the seasonal indexing approach that captures the variation in air quality caused due to meteorological changes have been used to provide short-term, real-time forecasts of PM2.5 concentration for one of the most polluted air quality control regions (AQCR) in the capital city of Delhi. Two MLMs—multi-linear regression and random forest—have been developed for using time series data for 1-h and 24-h average PM2.5 concentration. Short-term, real-time forecasts have been made using the developed models. Various model performance evaluation indices indicate satisfactory model performance. R2 values for the hourly and daily models varied between 0.95 and 0.72 and between 0.76 and 0.68 for the 1st to 5th h/day, respectively. The lagged values of PM2.5 concentration (persistence) and the hourly and daily indices are the most influential variables for the forecasts for immediate time steps. In contrast, seasonal indices become more important with the forecasting time horizon. The developed models can be used for making short-term, real-time air quality forecasts and issuing a warning when the pollution levels go beyond acceptable limits. [ABSTRACT FROM AUTHOR]

This paper presents the assessment of gaseous and particulate emissions from thermal power plants operating on different combustion technologies. Four thermal power plants operating on heavy furnace oil were selected for the study, among which three were based on diesel engine technology, while the fourth plant was based on oil-fired steam turbine technology. The stack emissions were monitored for critical air pollutants carbon monoxide, carbon dioxide, oxides of nitrogen, sulfur dioxide, particulate matter, lead, and mercury. The pollutant emissions were measured at optimum load conditions for a period of 6 months with an interval of 1 month. The results of stack emissions were compared with National Environmental Quality Standards of Pakistan and World Bank guidelines for thermal power plants, and few parameters were found higher than the permissible limits of emissions. It was observed that the emissions carbon monoxide, oxides of nitrogen, and particulate matters from diesel engine-based power plants were comparatively higher than the turbine-based power plants. The emissions of sulfur dioxide were high in all the plants, even the plants with different technologies, which was mainly due to high sulfur contents in fuel. [ABSTRACT FROM AUTHOR]

This study describes the field evaluation of a tailor-made new glass passive sampler developed for the determination of NO2, based on the collection on triethanolemine (TEA)-coated fibre filter paper. The sampler has been derived from a Palmes design. The overall uncertainty of the sampler was determined by using Griess–Saltzman ASTM D 1607 standard test method as a reference method. The agreement between the results of the passive sampler and the reference method was ±7.90% with the correlation coefficient of 0.90. Method precision in terms of coefficient of variance (CV) for three simultaneously applied passive samplers was 8.80%. The uptake rate of NO2 was found to be 2.49 ml/min in a very good agreement with the value calculated from theory (2.63 ml/min). Sampler detection limit was 1.99 μg/m3 for an exposure period of 1 week and the sampler can be stored safely for a period of up to 6 weeks before exposure. A comparison of the sampler performance was conducted against a commercially available diffusion tube (Gradko diffusion tube). The results from the applied statistical paired t test indicated that there was no significant difference between the performances of two passive samplers ( R 2 > 0.90). Also, another statistical comparison was carried out between the dark and transparent glass passive samplers. The results from the dark-colour sampler were higher than that from the transparent sampler (~25%) during the summer season because of the possible photodegradation of NO2–TEA complex. [ABSTRACT FROM AUTHOR]

This paper presents the impact of road user charging (RUC) on vehicle emissions through application of traffic assignment and pollutant emission models. It presents results of an analysis of five RUC schemes on vehicle emissions in Leeds, UK for 2005. The schemes were: a £3 inner ring road cordoncharge; a double cordon with a £2 inner ring road and a £1 outer ring road charge; and distance charges of 2, 10 and 20 p/km levied for travel within the outer cordon. Schemes were compared to a no charge option and results presented here. Emissions are significantly reduced within the inner cordon, whilst beyond the cordon, localised increases and decreases occur. The double cordon exhibits a similar but less marked pattern. Distance charging reduces city-wide emissions by 10% under a 2 p/km charge, 42–49% under a 10 p/km charge and 52–59% under a 20 p/km charge. The higher distance charges reduce emissions within the charge zone, and are also associated with elevated emissions outside the zone, but to a lesser extent than that observed for cordon charging. [ABSTRACT FROM AUTHOR]

The increasing trend of opencast coal mining in India tends to release huge amounts of dust. But there is no well-defined method of estimating dust emission due to different coal mining activities. This paper examines the sources of dust emission due to coal mining activities, and focuses on the quantification of dust emission with the development and use of emission factors. Because of their site-specific nature, emission factors developed for one site may not give the correct results for another site. In the present investigation, prediction equations are utilized for the development of emission factors. For the applications of this concept, one large opencast coal project of Bharat Coking Coal Ltd. (BCCL) was investigated, and the total amount of dust emitted due to different mining activities was calculated by the utilization of emission factor data, which was estimated to be 9368.2 kg/day. This paper also focuses on the significance of this study in the field of environmental protection and likely impacts of such study. The paper concludes that once the amount of dust generation is estimated, the impact on air quality can be assessed appropriately and a proper air-pollution control strategy can be developed. [ABSTRACT FROM AUTHOR]

Surface coal mining creates more air pollution problems with respect to dust than underground mining . An investigation was conducted to evaluate the characteristics of the airborne dust created by surface coal mining in the Jharia Coalfield. Work zone air quality monitoring was conducted at six locations, and ambient air quality monitoring was conducted at five locations, for a period of 1 year. Total suspended particulate matter (TSP) concentration was found to be as high as 3,723 μg/m3, respirable particulate matter (PM10) 780 μg/m3, and benzene soluble matter was up to 32% in TSP in work zone air. In ambient air, the average maximum level of TSP was 837 μg/m3, PM10 170 μg/m3 and benzene soluble matter was up to 30%. Particle size analysis of TSP revealed that they were more respirable in nature and the median diameter was around 20 μm. Work zone air was found to have higher levels of TSP, PM10 and benzene soluble materials than ambient air. Variations in weight percentages for different size particles are discussed on the basis of mining activities. Anionic concentration in TSP was also determined. This paper concludes that more stringent air quality standards should be adopted for coal mining areas and due consideration should be given on particle size distribution of the air-borne dust while designing control equipment. [ABSTRACT FROM AUTHOR]

This research paper aims at establishing baseline PM10 and PM2.5 concentration levels, which could be effectively used to develop and upgrade the standards in air pollution in developing countries. The relative contribution of fine fractions (PM2.5) and coarser fractions (PM10-2.5) to PM10 fractions were investigates in a megacity which is overcrowded and congested due to lack of road network and deteriorated air quality because of vehicular pollution. The present study was carried out during the winter of 2002. The average 24h PM10 concentration was 304 μg/m3, which is 3 times more than the Indian National Ambient Air Quality Standards (NAAQS) and higher PM10 concentration was due to fine fraction (PM2.5) released by vehicular exhaust. The 24h average PM2.5 concentration was found 179 μg/m3, which is exceeded USEPA and EU standards of 65 and 50 μg/m3 respectively for the winter. India does not have any PM2.5 standards. The 24 h average PM10-2.5 concentrations were found 126 μg/m3. The PM2.5 constituted more than 59% of PM10 and whereas PM10-PM2.5 fractions constituted 41% of PM10. The correlation between PM10 and PM2.5 was found higher as PM2.5 comprised major proportion of PM10 fractions contributed by vehicular emissions. [ABSTRACT FROM AUTHOR]

Air pollution has become a growing problem in megacities and large urban areas through out the globe, and transportation is recognized as the major source of air pollution in many cities, especially in developing countries. Contribution of automobiles is reported in the range of 40 to 80% of the total air pollution. The challenge facing megacities is how to reduce the adverse environmental impacts and other negative effects of transportation without giving up the benefits of mobility. The dilemma becomes most pressing under conditions of rapid urban growth, which is likely to increase travel demand significantly. The paper is aimed at understanding the problem of vehicular pollution vis-a-vis ambient air quality for a highly traffic affected megacity, Delhi, wherein, the contribution of transport sector was estimated to be as high as 72%. An effort has been made to review and evaluate the benefits (in terms of improved air quality) of the technological interventions/policies adopted for vehicular pollution control in Delhi. It also highlights the outcome of the efforts and suggests further improvements thereon. The importance of public participation and awareness are also discussed. The paper focuses on deriving the benefits of the implementation of management strategies, supported by scientific and technical data/interpretation, so that the people can realize and participate in the government's endeavor for clean city drive in a more effective manner. [ABSTRACT FROM AUTHOR]

Ever since the Regional Acidification Information and Simulation Model (RAINS) has been constructed, the treatment of uncertainty has remained an issue of major interest. In a recent review of the model performed for the Clean Air for Europe (CAFE) programme of the European Commission, a more systematic and structured uncertainty analysis has been recommended. This paper aims at contributing to the scientific debate how this can be achieved. Because of its complex structure on the one hand and limited research resources (time, computational capacities) on the other hand a full-blown uncertainty analysis in RAINS is hardly feasible. Therefore, all types of uncertainty require more efficient ways for uncertainty analysis. With respect to parameter uncertainty, we propose to focus research efforts for uncertainty analysis on key parameters. Among different approaches to select key parameters that have been discussed in the literature screening methods seem to be particularly appropriate for complex, deterministic Integrated Assessment models such as RAINS. Surprisingly, in Integrated Assessment modelling for air pollution problems of screening design have not been taken up so far. As a case study we consider the emission module of RAINS. We show that its structure allows for a straightforward and effective screening procedure [ABSTRACT FROM AUTHOR]

This paper deals with the application of l∞ (or minimax) optimization techniques to statistical modelling of high frequency air pollution data. The method was applied to ground-level ozone time-series data measured in Bordeaux over 4 years from 1998 to 2001. The aim of model building was to develop predictive models in order to provide forecasts of the maximal daily ground-level ozone concentration. Experimental results from this case study indicate that such techniques could be more appropriate than the commonly used 12 setting if only good estimation of high levels is of interest. When the free parameters are fitted by means of l∞ optimization techniques, the forecasting errors are more evenly distributed amongst the data points, resulting in a better estimation of high values. The paper compares the quality of forecasts produced by both a linear and a nonlinear model, using l2 and l∞ parameter optimization. [ABSTRACT FROM AUTHOR]

Dispersion of atmospheric contaminants in the vicinity of an isolated cubical model building was investigated in the field. A dual source/receptor technique was used in the experiments, which was proved to be very useful for the investigation of pollution dispersion. This experimental technique involved the simultaneous release of two different tracer gases from two different point sources, and the deployment of a FID (Flame Ionization Detector) co-located with a UVIC®(Ultra-Violet Ion Collector) detector. Both mean concentrations and concentration fluctuation statistics were examined. In this paper concentration fluctuation statistics are presented. The effect of the upwind source location on intermittency values and on the cumulative density function (cdf) is examined. The exact location of a source placed upwind of an obstacle has a very significant and complex effect on both mean concentrations and concentration fluctuations. As the lateral or vertical displacement between the two sources is increased, cross-correlation values between data taken simultaneously by two co-located detectors decrease rapidly. [ABSTRACT FROM AUTHOR]

An integrated method for the prediction of the spatial pollution distribution within a street canyon directly from a microscopic traffic simulation model is outlined. The traffic simulation package Paramics is used to model the flow of vehicles in realistic traffic conditions on a real road network. This produces details of the amount of pollutant produced by each vehicle at any given time. The authors calculate the dispersion of the pollutant using a particle tracking diffusion method which is superimposed on a known velocity and turbulence field. This paper shows how these individual components may be integrated to provide a practical street canyon pollution model. The resulting street canyon pollution model provides isoconcentrations of pollutant within the road topography. [ABSTRACT FROM AUTHOR]

Human activities are a key driver of air pollution, so it is hardly surprising that celebrations affect air quality. The use of fireworks contributes to high particulate concentrations in many parts of the world, with the Chinese Lunar New Year (spring festival) particularly noticeable, as firecrackers are traditionally used to drive off evil spirits. Fireworks lead to short-term peaks in the concentration of PM10, PM2.5 and SO. Regulatory actions that restrict the use of fireworks have been evident in China since the 1990s. This paper investigates the particulate concentrations in nine Chinese cities (Beijing, Chengdu, Chongqing, Tianjin, Xi'an, Nanjing, Shanghai, Guangzhou and Shenzhen, along with Hong Kong (a Special Administrative Region) and Taipei and Kaohsiung (Taiwan) with a particular focus on the celebrations of 2015. Extremely high concentrations of particulate matter were observed, with some sites revealing peak PM10 concentrations in excess of 1000 μg m in the early hours of the New Year. In Beijing, Tianjin and Chongqing, the activities caused high particulate matter concentrations at most sites throughout the city. These peaks in particulate load in the early hours of Chinese New Year do not appear to be closely related to meteorological parameters. However, in cities where fireworks appear to be better regulated, there are fewer sharp pollution peaks just after midnight, although lowered air quality can still be found in the outer parts of some cities, remote from regulatory pressures. A few cities seem to have been effective at reducing the impact of the celebrations on air quality, with Nanjing a recent example. An increasing focus on light displays and electric lanterns also seems to offer a sense of celebration with much reduced impacts on air quality. [ABSTRACT FROM AUTHOR]

The following paper presents an assessment of health risks associated with air polluted with respirable asbestos fibers in towns of southwest Poland. The aim of the work was to determine whether or not any prevention measures are necessary in order to reduce the level of exposure to the pollutant. The risk assessment was carried out based on the air analyses and the latest asbestos toxicity data published by the Environmental Protection Agency (US EPA), USA and Office of Environmental Health Hazard Assessment (OEHHA). It was found that in some sites, the concentration of the asbestos fibers exceeded the acceptable levels, which should be a reason of special concern. The highest concentration of asbestos was found in town centers during the rush hours. In three spots, the calculated maximum health risk exceeded 1E-04 which is considered too high according to the adopted standards. So far, it has not yet been possible to find a reasonable method of ensuring the hazard reduction. [ABSTRACT FROM AUTHOR]

Abundance of CaCO rich soil dust is a typical feature of atmospheric environment in the Indian region. During prevailing dry weather conditions, dustfall is deposited onto the foliar surfaces of plant affecting their morphology, stomata and the levels of biochemical constituents. This study reports the chemical characteristics of dustfall, its effect on foliar morphology and biochemical constituents of a medicinal plant ( Morus alba) at two sites which are differentiated on the basis of landuse pattern, viz., (i) residential, Jawaharlal Nehru University (JNU), and (ii) industrial, Sahibabad (SB), located in the National Capital Region (NCR) of Delhi. Dustfall was characterized for major anions (F, Cl, NO and SO) and cations (Na, NH, K, Mg and Ca). Biochemical parameters such as chlorophyll a, chlorophyll b, total chlorophyll, carotenoid, proline and ascorbic acid were determined in foliar samples. The results showed that the dustfall fluxes of all the major ions were found to be higher at the industrial site (SB) as compared to the residential site (JNU). Foliar analysis revealed that the levels of biochemical parameters were more affected at SB site due to higher levels of dust SO contributed by various anthropogenic sources resulting in more stressful conditions affecting the biochemistry of the plant. The possible entry pathways for dust SO into foliar cells are also discussed in the paper. It was noticed that the deposition of urban dust was responsible for the damage of trichome, epidermis, cuticle and stomatal guard cells significantly affecting foliar morphology. SB exhibited more damage to these morphological parts suggesting that industrial dust is harmful to the plants. [ABSTRACT FROM AUTHOR]

Over the past decade, an increased tendency to consume meals at dining facilities outside the home has been highlighted; moreover, meals supplied in food businesses have been involved in many foodborne disease outbreaks. Therefore, microbial air contamination in food processing facilities could be a concern and an increase of microbial loads could represent a risk factor, especially for the potential contamination of foods due to undesirable spoiling and pathogenic bacteria. In this paper, the results of an 11-year microbiological monitoring of air quality in a university canteen are reported. The study, which started in the year 2000, was performed within a hazard analysis and critical control point (HACCP) plan implementation of a canteen that produces about 1,000 meals a day in order to verify the effectiveness of corrective actions on the indoor air quality. The primary food preparation room, the kitchen, and three cold rooms underwent air sampling by using a calibrated impaction sampler. Our investigation detected a general and progressive improvement in the air quality of the canteen since the beginning of the study, thus suggesting the appropriateness of the corrective action undertaken during the HACCP implementation program. [ABSTRACT FROM AUTHOR]

A correct characterization of the status and trend of forest condition is essential to support reporting processes at national and international level. An international forest condition monitoring has been implemented in Europe since 1987 under the auspices of the International Co-operative Programme on Assessment and Monitoring of Air Pollution Effects on Forests (ICP Forests). The monitoring is based on harmonized methodologies, with individual countries being responsible for its implementation. Due to inconsistencies and problems in sampling design, however, the ICP Forests network is not able to produce reliable quantitative estimates of forest condition at European and sometimes at country level. This paper proposes (1) a set of requirements for status and change assessment and (2) a harmonized sampling strategy able to provide unbiased and consistent estimators of forest condition parameters and of their changes at both country and European level. Under the assumption that a common definition of forest holds among European countries, monitoring objectives, parameters of concern and accuracy indexes are stated. On the basis of fixed-area plot sampling performed independently in each country, an unbiased and consistent estimator of forest defoliation indexes is obtained at both country and European level, together with conservative estimators of their sampling variance and power in the detection of changes. The strategy adopts a probabilistic sampling scheme based on fixed-area plots selected by means of systematic or stratified schemes. Operative guidelines for its application are provided. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to develop an air pollution model that is independent from pollution monitoring sites and highly accurate through space and time. Total carbon monoxide concentration is computed with the use of traffic flow data, vehicle speed and dimensions, emission rates, wind speed, and temperature. The data are interpolated using a geographic information system universal kriging technique, and the end results produce state level air pollution maps with high local accuracy. The model is validated against Environment Protection Agency (EPA) pollution data. Overall, the model has 71 % agreement with EPA, overestimating values of carbon monoxide for less than 1 ppm. The model has three advantages over already assessed air pollution models. First, it is completely independent of any air pollution monitoring stations; thus, possible temporary or permanent unreliability or lack of the data is avoided. Second, being based on a 5,710 traffic count network, the problem of remote places coverage is avoided. Third, it is based on a straightforward equation, where minimal preprocessing of traffic and climatic data is required. [ABSTRACT FROM AUTHOR]

In this paper, COSMO numerical weather prediction model is used to simulate land-sea breeze circulation in the north coast of Oman and to analyze the variability of the PBL depth. Typical summer day 1st August 2009 and winter day 1st January 2009 were simulated and verified by surface observations. The COSMO model was initialized using the German global model which runs as 40 km resolution and 40 vertical layers. The simulations show differences in the onset, strength, and inland penetration of the sea breeze. The high sea-land thermal contrast in the summer season induces earlier onset and progression of the sea breeze with larger horizontal and vertical extents. The summer and winter inland penetration is simulated around 150 and 65 km, respectively. The PBL depth was defined from the verticals profiles of the turbulence kinetic energy (TKE). The simulated PBL height varied at 1,600 m in summer and 700 m in the winter case. These results are in general in good agreement with the simulations of the National Centers for Environmental Prediction Global Data Assimilation System (NCEP-GDAS) model. The dispersive ability of the atmospherewas tested through the vertical mixing coefficient (VMC) generated by NCEP-GDAS simulations. The VMC during the summer simulated case is approximately four times larger than the respective one during the winter case. Consequently, during the advection of winter sea breeze, a shallow dome less than 100 m is shaped and forms an obstacle to the airpollutant dispersion. [ABSTRACT FROM AUTHOR]

Epidemiological studies typically use monitored air pollution data from a single station or as averaged data from several stations to estimate population exposure. In industrialized urban areas, this approach may present critical issues due to the spatial complexities of air pollutants which are emitted by different sources. This study focused on the city of Taranto, which is one of the most highly industrialized cities in southern Italy. Epidemiological studies have revealed several critical situations in this area, in terms of mortality excess and short-term health effects of air pollution. The aims of this paper are to study the variability of air pollutants in the city of Taranto and to interpret the results in relation to the applicability of the data in assessing population exposure. Meteorological and pollution data (SO2, NO2, PM10), measured simultaneously and continuously during the period 2006-2010 in five air quality stations, were analyzed. Relative and absolute spatial concentration variations were investigated by means of statistical indexes. Results show significant differences among stations. The highest correlation between stations was observed for PM10 concentrations, while critical values were found for NO2. The worst values were observed for the SO2 series. The high values of 90th percentile of differences between pairs of monitoring sites for the three pollutants index suggest that mean concentrations differ by large amounts from site to site. The overall analysis supports the hypothesis that various parts of the city are differently affected by the different emission sources, depending on meteorological conditions. In particular, analysis revealed that the influence of the industrial site may be primarily identified with the series of SO2 data which exhibit higher mean concentration values and positive correlations with wind intensity when the monitoring station is downwind from the industrial site. Results suggest evaluating the population exposure to air pollutants in industrialized cities by taking into account the possible zones of influence of different emission sources. More research is needed to identify an indicator, which ought to be a synthesis of several pollutants, and take into account the meteorological variables. [ABSTRACT FROM AUTHOR]

Dust from haul and transport roads are the major source of air pollution in opencast coal mining areas. Dust generated during mining operations pollutes air which causes different health problems. Various available techniques are implemented in the field to minimize and control dust in mining areas. However, they are not very effective because dust deposited on road surfaces are not removed by these techniques. For effective control of dust in opencast mining areas, it has to be regularly collected from road surfaces and may be converted into solid form, and subsequently can be used as a domestic fuel considering its physicochemical properties. The present paper describes a comparative study of qualitative and quantitative aspects of road dust samples of four coalfields of India. The pH of the dust was found to be in the range of 5.1-7.7. Moisture, ash, volatile matter, fixed carbon, water-holding capacity, bulk density, and specific gravity of dust samples were found to be in the range of 0.5-3.0%, 45-76%, 12.6-20.0%, 10.2-45.3%, 21.17-31.71%, 1.15-1.70, and 1.73-2.30 g cm, respectively. Observing the overall generation and characteristics of coal dust, it is suggested that coal dust from haul and transport roads of mining areas can be effectively collected and used as domestic fuel. [ABSTRACT FROM AUTHOR]