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Kolleru Lake, a wetland located in India, is one of the largest natural freshwater lakes and is an important sanctuary for indigenous and migratory birds, particularly in winter seasons. The lake is located between latitudes 16°32′ and 16°47′N and longitudes 81°05′ and 81°27′E. The lake is connected to the sea through the Upputeru River, at a distance of 60 km. The lake water is mainly used for drinking water, agriculture, fishing and aquaculture purposes. The lake ecosystem is deteriorating due to the industrial, agricultural and aquacultural activities. High volume sampler was used for the collection of air pollutants, namely suspended particulate matter, nitrogen oxide and sulfur dioxide from the lake at four locations over a period of one year. Water samples were collected from the lake in three seasons in a year over a period of three years and analyzed for water quality parameter, namely total suspended solids, hardness, chloride, sodium, chemical oxygen demand and biochemical oxygen demand. The aim of this study is to create the air and water pollution maps of Kolleru Lake using geographic information systems (GIS) for the better management of Kolleru Lake to control the pollution and also to avoid the risk of air and water pollutants on humans, aquatic organisms, birds and plants.

The purpose of this study is to monitor suspended particulate matter (SPM), PM2.5 and source apportionment study for the identification of possible sources during the year 2018–2019 at Raipur, India.

Source apportionment study was performed using a multivariate receptor model, positive matrix factorization (PMFv5.0) with a view to identify the various possible sources of particulate matter in the area. Back-trajectory analysis was also performed using NOAA-HYSPLIT model to understand the origin and trans-boundary movement of air mass over the sampling location.

Daily average SPM and PM2.5 aerosols mass concentration was found to be 377.19 ± 157.24 µg/m³ and 126.39 ± 37.77 µg/m³ respectively. SPM and PM2.5 mass concentrations showed distinct seasonal cycle; SPM – (Winter ; 377.19 ±157.25 µg/m?) > (Summer; 283.57 ±93.18 µg/m?) > (Monsoon; 33.20 ±16.32 µg/m?) and PM2.5 – (Winter; 126.39±37.77 µg/m³) > (Summer; 75.92±12.28 µg/m³). Source apportionment model (PMF) have been applied and identified five major sources contributing the pollution; steel production and industry (68%), vehicular and re-suspended road dust (10.1%), heavy oil combustion (10.1%), tire wear and brake wear/abrasion (8%) and crustal/Earth crust (3.7%). Industrial activities have been identified as major contributing factor for air quality degradation in the region.

Chemical characterization of aerosols and identification of possible sources will be helpful in abatement of pollution and framing mitigating strategies. It will also help in standardization of global climate model.

The findings provide valuable results to be considered for controlling air pollution in the region.

The purpose of this paper is to establish mass balance model and predict the concentration and diameter distribution of indoor suspended particulate matters (SPM).

Taking the small offices and residences for a research objective, this paper analyzes the major factors to affect the concentration and diameter distribution of indoor SPM, founds the deposition ratio model, the penetration factor model and the mass balance model to predict the concentration and diameter distribution of indoor SPM. According to the real‐time measuring data, the feature of building defence structure and the concentration and diameter distribution of outdoor SPM, the deposition model, the penetration model and indoor air capacity are used as input parameter of the mass balance model.

The size of defence in natural ventilation, the pressure difference of both sides and the friction velocity have less influence on the concentration and diameter distribution of indoor SPM, but the concentration and diameter distribution of outdoor SPM mainly affects that of indoor SPM. Indoor particle concentration change with outdoor particle concentration, and less than later because of indoor particle deposition. The prediction results are basically in agreement with the measuring data.

Real‐time and accuracy of measuring data of outdoor SPM are the main limitations which the prediction model are simulated.

The prediction results can provide scientific theory basis for making environmental standards of particulate matter and the control of indoor air quality.

A new method to predict the concentration and diameter distribution of indoor SPM.

There has been a growing concern about air quality because in recent years, industrial and vehicle emissions have resulted in unsatisfactory human health conditions. There is an urgent need for the measurements and estimations of particulate pollutants levels, especially in urban areas. As a contribution to this issue, the purpose of this paper is to use data from measured concentrations of particulate matter and meteorological conditions for the predictions of PM₁₀.

The procedure included daily data collection of current PM₁₀ concentrations for the city of São Carlos-SP, Brazil. These data series enabled to use an estimator based on artificial neural networks. Data sets were collected using the high-volume sampler equipment (VFA-MP10) in the period ranging from 1997 to 2006 and from 2014 to 2015. The predictive models were created using statistics from meteorological data. The models were developed using two neural network architectures, namely, perceptron multilayer (MLP) and non-linear autoregressive exogenous (NARX) inputs network.

It was observed that, over time, there was a decrease in the PM₁₀ concentration rates. This is due to the implementation of more strict environmental laws and the development of less polluting technologies. The model NARX that used as input layer the climatic variables and the PM₁₀ of the previous day presented the highest average absolute error. However, the NARX model presented the fastest convergence compared with the MLP network.

The presentation of a given PM₁₀ concentration of the previous day improved the performance of the predictive models. This paper brings contributions with the NARX model applications.

Emission of the suspended particulate matter (SPM) from cement factories results in a wide range of negative health effects to its workers. It induces substantial cost incurred by them in the form of wage loss and mitigation cost. Therefore, the purpose of this paper is to estimate emission-induced cost of illness (COI) and the share of this cost that could be saved through limiting the current emission level at national safety standard.

COI approach which accounts workers’ wage loss and mitigation cost due to emission-induced illness was used in this study. A sample of 120 workers from three factories followed by 40 from each was randomly surveyed for collecting information on their health status and mitigation cost. It covered almost 10 percent of cement factory workers in the south-west region of Bangladesh. In addition, factory-specific emission data were also collected from the Department of Environment for addressing the study objective.

It revealed that the average level of SPM emission by these factories which was almost three times higher than the national safety standard induced 34.39 million Bangladeshi Taka (BDT) (USD0.46 million) as COI paid by workers yearly. It accounted around 28 percent of their annual income of which 64 percent worth BDT22.16 (USD0.30) million could be saved by meeting the standard annually.

This study provides insights into the essence of regulating cement industrialists toward meeting the national safety standard of emission.

The eastern coast of Saudi Arabia has one of the most corrosive environments in the world. Dhahran is therefore an ideal location for the study of atmospheric corrosion. One out of every seven cars in the region is corroded. The atmosphere is contaminated by SO₂ and a high concentration of suspended particulate matter (SPM) containing sand, salt and carbon particles, exceeding the World Health Organization (WHO) and Middle East Environmental Protection Agency (MEPA) limits most of the time. Corrosion proceeds in the Dhahran atmosphere at a R.H. as low as 40 per cent. At locations close to the sea (1.5 Km), β‐FeOOH is the major corrosion product and α‐ and γ‐FeOOH with some aluminates, silicates and hydrocarbons the minor products. At a location remote from the sea α‐ and γ‐FeOOH are found to be the major corrosion products as shown by XRD and FTIR spectroscopy. The concentration of hydrogen ions, chloride ions and SO₂ appears to control the corrosion process during the initial exposure period up to one year and the adsorption of anions on the corrosion product films in the later period.

The purpose of this paper is to explore the environment around the rail track at different sites in Nile Delta region, Egypt, through the measurements of the air pollutants and corrosive ionic species present in surface soil and also to investigate the impact of the existing contaminants on the composition of iron rust formed on the rail head surface at these sites and then the durability of rail itself.

The soil characterization was studied by means of sieve shakers, pH meter, conductivity meter and ion chromatography instrument. Scanning electron microscopy, energy dispersive spectrometry, Fourier transform infrared spectroscopy and X-ray diffraction were used to characterize the rust layer formed on the rail head surface.

The results showed the relation between the contaminants and the composition of the rust layer. Magnetite and goethite were the major phases identified in the rust layers. Akaganeite was detected in the marine atmosphere. Iron sulfide and iron oxide nitrate hydroxide were detected in environments rich in H₂S and NO₂ gases, respectively. The appearance of phases like FeCl₂ and FeOCl only at marine atmospheres reflects that the corrosive species in suspended particulate matter like chloride ion have a higher effect on the rust composition of the rail head surface than that in surface soil layer.

This paper revealed the impact of air and soil contaminants on the composition of rust layer on the rail head surface and may provide guidance for the durability of rails and the necessity for their preservation.

A model for the decohesion of aggregates of suspended particulate material in a binding matrix is developed. In the model cohesive zones which envelop each particle individually are introduced at the particulate/binder interface. During progressive loading, the deterioration of the cohesive zones is initiated if constraints placed on the microstress fields are violated. In order for the material behavior to be energetically admissible, the deterioration of the material at a point is in the form of a reduction of the elasticity tensor’s eigenvalues at that point. The material within the cohesive zones deteriorates until the constraints are met. In order to isolate and study the effects of interfacial deterioration, outside of the cohesive zones, the material is unaltered. Mathematical properties of the model, as well as physical restrictions, are discussed. Numerical simulations are performed employing the finite element method to illustrate the approach in three‐dimensional applications.

Environmental pollution and corresponding adverse health impacts have now become a significant concern for the entire planet. In this regard, analysts and experts are continually formulating policies to reduce environmental pollution and improve natural ecological conditions. To aid in coping with the ecological predicament, a framework has been developed in the present study to inspect the adverse environmental impacts and related health issues of coal mining.

The parameters for this study have been identified through a review of the literature and finalized 23 critical parameters of air, water, land and soil, and noise related to coal mining by consultation with experts from industry and academia. Finally, the parameters have been categorized in accordance with the level of threat they pose to the environment by assigning weight using the Bradley–Terry model considering attitudinal data acquired by a questionnaire survey.

It is found that coal mining has a relatively higher impact on four attributes of “air pollution” (suspended particulate matter [SPM], respiratory particulate matter [RPM], sulfur dioxide [SO₂] and oxides of nitrogen [NO_x]), followed by “land and soil pollution” (deforestation and surface structure diversion), “noise pollution” (vehicle movement) and “water pollution” (water hardness, total solids (TSS/TDS) and iron content). It is also found that raising the air concentration of SPM and RPM results in increased respiratory and cardiopulmonary mortality. Therefore, reducing dust concentrations into the air generated during coal mining is recommended to reduce air pollution caused by coal mining, which will reduce contamination of water and land and soil.

The model built in this study is a hypothesized model that relied on the experts' opinions considering the parameters of coal production only. However, the parameters related to the usage of coal and its consequences have been excluded. Further, only industrial and academic experts were considered for this study; however, they excluded local people, coal mining personnel, policy authorities, etc. Therefore, the study findings might differ in real circumstances. The research can further be reproduced by considering the parameters related to the use of coal and its consequences, considering the opinions of the local people, coal mining personnel and policy authorities.

Categorizing the parameters according to the threat they pose to the environment due to coal mining can help the decision-maker develop an effective policy to reduce environmental pollution due to coal mining by considering the parameters on a priority basis. In addition, the results further help the decision-makers to assess the environmental impact of coal mining and take necessary action.

The study has developed a framework using the Bradley–Terry model to categorize the environmental parameters of coal mining to develop effective environmental policies, which are original and unique in nature.