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Dust (particulate matters) is very dangerous to our health as it is not visible with our naked eyes. Emissions of dust concentrations in the natural environment can occur mainly by road traffic, constructions and dust generating working environments. The purpose of this paper is to assess the ambient dust pollution status and to find out the association between PM concentrations and other determinant factors such as wind speed, ambient temperature, relative humidity and traffic congestion.

A cross-sectional study was conducted for two consecutive months (June and July, 2016) at a residential site (Defence Services Liver Hospital, Mingaladon) and a commercial site (Htouk-kyant Junction, Mingaladon) based on WHO Air Quality Reference Guideline Value (24-hour average). Hourly monitoring of PM2.5 and PM10 concentration and determinant factors such as traffic congestion, wind speed, ambient temperature and relative humidity for 24 hours a day was performed in both study sites. CW-HAT200 handheld particulate matters monitoring device was used to assess PM concentrations, temperature and humidity while traffic congestion was monitored by CCTV cameras.

The baseline PM2.5 and PM10 concentrations of Mingaladon area were (28.50±11.49)µg/m³ and (52.69±23.53)µg/m³, means 61.48 percent of PM2.5 concentration and 54.92 percent of PM10 concentration exceeded than the WHO reference value during the study period. PM concentration usually reached a peak during early morning (within 3:00 a.m.-5:00 a.m.) and at night (after 9:00 p.m.). PM2.5 concentration mainly depends on traffic congestion and temperature (adjusted R²=0.286), while PM10 concentration depends on traffic congestion and relative humidity (adjusted R²=0.292). Wind speed played a negative role in both PM2.5 and PM10 concentration with r=−0.228 and r=−0.266.

The air quality of the study area did not reach the satisfiable condition. The main cause of increased dust pollution in the whole study area was high traffic congestion (R²=0.63 and 0.60 for PM2.5 and PM10 concentration).

PM10 is one of the most dangerous air pollutants which is harmful to the ecological system and human health. Accurate forecasting of PM10 concentration makes it easier for the government to make efficient decisions and policies. However, the PM10 concentration, particularly, the emerging short-term concentration has high uncertainties as it is often impacted by many factors and also time varying. Above all, a new methodology which can overcome such difficulties is needed.

The grey system theory is used to build the short-term PM10 forecasting model. The Euler polynomial is used as a driving term of the proposed grey model, and then the convolutional solution is applied to make the new model computationally feasible. The grey wolf optimizer is used to select the optimal nonlinear parameters of the proposed model.

The introduction of the Euler polynomial makes the new model more flexible and more general as it can yield several other conventional grey models under certain conditions. The new model presents significantly higher performance, is more accurate and also more stable, than the six existing grey models in three real-world cases and the case of short-term PM10 forecasting in Tianjin China.

With high performance in the real-world case in Tianjin China, the proposed model appears to have high potential to accurately forecast the PM10 concentration in big cities of China. Therefore, it can be considered as a decision-making support tool in the near future.

This is the first work introducing the Euler polynomial to the grey system models, and a more general formulation of existing grey models is also obtained. The modelling pattern used in this paper can be used as an example for building other similar nonlinear grey models. The practical example of short-term PM10 forecasting in Tianjin China is also presented for the first time.

The quantitative assessment of environmental effects should be carried out using the indicators that ensure the best objectivity and efficiency of the environmental impact assessment (EIA) process. The aim of this paper is to clarify the effectiveness of the methods used for prognoses of air pollution from roads. The benchmark method proposed in this paper is based on theoretical knowledge and on analysis of data collected from EIA reports.

The paper includes a literature review and developing of the benchmark method. This research was based on data dispersion analysis, determination of benchmarks, usage of regression method, as well as confrontation of different methods of determination of air emission concentration used in EIA.

The simplified model has been designed for determination of concentration of dust emission in the air from motor roads, the building or reconstruction of which is planned. The results indicate that the benchmark method for determination of air pollution with particulate matter PM10 has been elaborated, which can be used for environmental impact evaluation in motor road construction. The method has been elaborated relying on measurement data of existing motor roads and displays a high level of probability of credibility.

The method is simpler and less time‐consuming than the currently used calculation model. It produces a more precise result than the calculation model. It is outstanding and important since the further development of motor road projects can undoubtedly be judged by the results of EIA.

The results of this research provide a rational and comparative approach for finding the methods of determination of the air emission concentration used in EIA in producing the credible outcome. The results reported in the research show existing problems with calculation of emissions from roads. The proposed benchmark method is simple, easy to use and credible.

The purpose of this paper is to assess the exposure of cooks in rural India (55 households) to the indoor air pollution levels emitted from burning of different fuels, i.e. cow dung, wood, liquefied petroleum gas (LPG) and propane natural gas(PNG) kerosene for cooking purposes.

Indoor air quality was monitored during cooking hours in 55 rural households to estimate the emissions of PM10, PM2.5, CO, NO₂, VOCs and polyaromatic hydrocarbons (PAHs). While, PM10 and PM2.5 were monitored using personal dust samplers on quartz filter paper, CO and VOCs were monitored using on line monitors. The PM10 and PM2.5 mass collected on filter papers was processed to analyse the presence of PAHs using GC.

Results revealed that cow dung is the most polluting fuel with maximum emissions of PM10, PM 2.5, VOCs, CO, NO₂ and Benzene followed by wood and kerosene. Interestingly kerosene combustion emits the highest amount of PAHs. Emissions for all the fuels show the presence of carcinogenic PAHs which could be a serious health concern. The composition of LPG/PNG leads to reductions of pollutants because of better combustion process. LPG which is largely propane and butane, and PNG which is 90 per cent methane prove to be healthier fuels. Based on the results, the authors suggest that technological intervention is required to replace the traditional stoves with improved fuel efficient stoves.

The prevailing weather condition and design of the kitchen in these rural houses severely affect the concentration of pollutants in the kitchen as winter season combined with inadequate ventilation leads to reduced dispersion and accumulation of air pollutants in small kitchens.

The present study provides a detailed analysis of impact of widely‐used cooking practices in India. Even today, countries such as India rely on biomass for cooking practices exposing the cooks to high level of carcinogenic pollutants. Further, women and girls are the most threatened group as they are the primary cooks in these rural Indian settings. Based on the results, the authors suggest that technological as well as policy intervention is required to replace the traditional stoves with improved fuel efficient stoves.

The aim of this paper is to develop the accurate computer method of predicting the average PM10 pollution for the next day on the basis of some measured atmospheric parameters, like temperature, humidity, wind, etc. This method should be universal and applicable for any place under consideration.

The paper presents the new approach to the accurate forecasting of the daily average concentration of PM10. It is based on the application of the ensemble of neural networks and wavelet transformation of the time series, representing PM10 pollution.

On the basis of numerical experiments, the paper finds that application of many neural predictors cooperating with each other can significantly improve the quality of results. The paper shows that the developed forecasting system checked on the data of PM10 pollution in Warsaw generated good overall accuracy of prediction in terms of root mean squared error, mean absolute error and mean absolute percentage error.

The main novelty of the proposed approach is the application of the wavelet transformation and many neural networks organized in the form of ensemble. The individual neural predictors are integrated into one forecasting system using different forms of integrations, including the blind source separation method and neural‐based integration.

This study aims to examine the impact of local air pollution on housing prices in Lebanon.

The authors apply a hedonic pricing approach using a unique data set from Lebanon. To account for non-linearities in pricing, the authors use three different functional regression forms for the hedonic model approach. The authors also deal with potential omitted variable bias by estimating a hedonic frontier specification.

The authors find that, in all specifications, air pollution negatively and significantly affects housing prices. The estimated marginal willingness to pay for a one microgram per cubic meter change in particulate matter (PM10) concentration ranges between 2.88% and 3.18% of mean housing prices. The authors also provide evidence of a negative pricing gradient away from the city center, landing support for the monocentric urban development hypothesis.

Given the lack of a data set linking household socioeconomic characteristics with housing data, the authors only consider the first-stage hedonic model.

The proposed hedonic pricing regression approximates a housing pricing equation that can be used by policymakers.

The findings suggest that pollution is a significant factor in household behavior in Lebanon.

This paper adds to the scant literature studying the effects of air pollution on housing prices in developing countries. To the best of the authors’ knowledge, this is the first paper to study the impact of pollution on housing prices in a country in the Middle East and North Africa Region.

The purpose of this paper is to investigate whether a statistically significant relationship exists between environmental quality, as measured by consumption-related air pollution, and public debt in Europe. In addition, since the debt burden is one of the most important indicators of fiscal soundness within the European Union (EU) Treaty and the subsequent fiscal compact, the authors propose a simple test to determine whether participation in EU Treaties has shaped the empirical relationship between fiscal policy/public debt and environmental performance.

To this end, the authors built a panel data set that covers 24 European countries over the period 1996–2015.

The aspect that the authors want to underline is a possible trade off, which is confirmed in the empirical analysis, between the public finance equilibrium and the maintenance of a public good such as air quality. However, there are important non-linearities that shape the interaction between public debt and environmental pollution. Similarly, threshold effects arise when the authors examine the interaction between EU regulation and public debt and when the authors separately examine high debt and low debt countries. When the authors account for the stabilization rules introduced by EU Treaties, a negative effect on pollution is evident; in this way, fiscal consolidation limits the positive effect of fiscal policy.

The results point out the existence of a potential trade-off between the role of EU as a regulator aiming to mitigate environmental pollution, and its role within the Stability and Growth Pact. The analysis highlights that fiscal consolidation policies, while facilitating the achievement of macroeconomic stability within EU, might have a negative side effect on the environment quality, which spreads beyond the borders of one single country.

While a number of studies have suggested that fiscal spending might contribute to the level of pollution in European countries, there is scant evidence of the effect of public debt on environmental performance. This lack of scientific knowledge is a serious shortcoming, since it may allow for an underrepresentation of the wide-ranging consequences of stabilization programmes targeting the debt-to-GDP ratio, which could affect environmental quality.

Purpose – The vulnerability and adaptive capacities of cities in Latin America have received relatively less attention compared to other regions of the world. This chapter seeks to address these gaps by (a) examining vulnerability to the health impacts from air pollution and temperature, and exploring whether socioeconomic factors between neighborhoods differentiate these risks within the cities of Bogota, Buenos Aires, Mexico City, and Santiago and (b) assessing the capacity of urban populations to perceive and respond to vulnerability and risk.

Design/methodology/approach – Because of the complex nature of vulnerability, we combined a set of quantitative and quantitative methods and data to determine whether and under what conditions the people in these cities are vulnerable (e.g., Time Series Analysis, Generalized Linear Model, and statistical correlations of exposure and human mortality with socioeconomic vulnerability).

Findings – We found high levels of PM10, ozone, and other criteria air pollutants in three cities for which we had data. However, the pattern of their impacts on health depends on the particulars of pollutant levels and atmospheric and weather conditions of each city. Our results reflect the varied facets of urban vulnerability and shed light on the nature of the associated human health risks. Although wealthy populations have access to education, good quality housing, and health services to mitigate some environmental risks, overall the data show that health impacts from air pollution and temperature in the study cities do not necessarily depend on socioeconomic differentiations.

Research limitations/implications – Although we sought to use quantitative and qualitative methods, given the complexity of the research, it has proven difficult to fully explore these issues across scales and with a full accounting of local context.

Practical implications – Our findings show that wealthy and educated populations may be equally at risk to the health implications of air pollution. Policies designed to mitigate these risks should not use socioeconomic characteristics as predictors of a population's risk in relation to air pollution.

Originality/value – This research contributes valuable insights into the dynamics of vulnerability to air pollution in Latin American cities, a region that has been historically underrepresented in empirical studies of urban risk. We have also combined a range of methods and approaches to improve our understanding of the multifaceted nature of urban vulnerability to global environmental change.

Severe airborne particulate pollution frequently occurs over the North China Plain (NCP) region in recent years. To better understand the characteristics of carbonaceous components in particulate matter (PM) over the NCP region.

PM samples were collected at a typical area affected by industrial emissions in Handan, in January 2016. The concentrations of organic carbon (OC) and elemental carbon (EC) in PM of different size ranges (i.e. PM_2.5, PM₁₀ and TSP) were measured. The concentrations of secondary organic carbon (SOC) were estimated by the EC tracer method.

The results show that the concentration of OC ranged from 14.9 μg m⁻³ to 108.4 μg m⁻³, and that of EC ranged from 4.0 μg m⁻³ to 19.4μg m⁻³, when PM_2.5 changed from 58.0μg m⁻³ to 251.1μg m⁻³ during haze days, and the carbonaceous aerosols most distributed in PM_2.5 rather than large fraction. The concentrations of OC and EC PM_2.5 correlated better (r = 0.7) than in PM_2.5−10 and PM_>10, implying that primary emissions were dominant sources of OC and EC in PM_2.5. The mean ratios of OC/EC in PM_2.5, PM_2.5–10 and PM_>10 were 4.4 ± 2.1, 3.6 ± 0.9 and 1.9 ± 0.7, respectively. Based on estimation, SOC accounted for 16.3%, 22.0% and 9.1% in PM_2.5, PM_2.5–10 and PM_>10 respectively.

The ratio of SOC/OC (48.2%) in PM_2.5 was higher in Handan than those (28%–32%) in other megacities, e.g. Beijing, Tianjin and Shijiazhuang in the NCP, suggesting that the formation of SOC contributed significantly to OC. The mean mass absorption efficiencies of EC (MACEC) in PM₁₀ and TSP were 3.4 m² g⁻¹ (1.9–6.6 m² g⁻¹) and 2.9 m² g⁻¹ (1.6–5.6 m² g⁻¹), respectively, both of which had similar variation patterns to those of OC/EC and SOC/OC.