Pollution Characteristics of Particulate Matter (PM2.5 and PM10) and Constituent Carbonaceous Aerosols in a South Asian Future Megacity

Comparison of Ambient Air Quality among Industrial and Residential Areas of a Typical South Asian city

The rapid increase in population growth due to industrialization and urbanization has resulted in air quality deterioration in Pakistan. Consequently, a considerable increase has been seen in the types of sources of air pollutants. However, the air quality of the country has deteriorated in the absence of management capabilities against air quality. Evidence from numerous governmental organizations and international bodies has specified that the environment, health, and quality of life are at high risk due to air pollution. Although the government of Pakistan established the Pakistan Clean Air Program, along with continuous monitoring stations to manage the quality of ambient air, air quality values have not yet been achieved. The present investigations were made in the city of Faisalabad in selected locations. Sampling of a 24 h average was done for selected sites. The air quality parameters such as NO2, SO2, COx, O2, noise level, and suspended particulate matter (SPM) were measured at two locations, i.e., Khurrianwala and Liaqatabad in the Faisalabad District. The measured values of air quality parameters were compared with national environmental quality standards (NEQS). Air pollutants such as SPM, SO2, and noise levels were found to be significantly higher than the 24-h standards of NEQS, which poses harmful effects on the quality of air and health, whereas the O2 concentration was found to be lower than the normal values, and NO2 and COx values were normal. The SO2, CO2, noise level, SPM, and O2 values ranged from 418–652 and 423–661 µg/m3, 3.03–3.44 and 3.08–3.51 mg/m3, 68–73 and 69–75 dB, 555–667 and 581–682 µg/m3, and 19.5–20 and 19.5–20.3 % for summer and winter season, respectively, as compared to standard values (150 µg/m3, 10 mg/m3, 65 dB, 550 µg/m3 and 21%). After the complete analysis of the selected locations, it was concluded that the ambient air quality of this area is severely degraded due to industrial as well as other commercial activities. These significant variations in air quality parameters suggest that there is a need to check the air quality regularly to take appropriate measures for reducing ambient air pollutants, especially in industrial areas as well as commercial areas.

Suppression Effect of Waterborne Polymer on Soil Used for Backfilling at Construction Site

To improve the dust control efficiency of soil for backfilling at construction sites, a novel waterborne polymer was used as a dust suppressant, and the dust emission model was created to control the effect of a large-scale field. The results showed that the waterborne polymer could improve the water retention efficiency of soil for backfilling, and the average water content was 2.18 times that of the watered samples, significantly delaying water evaporation. The compressive strength of soil for backfilling reached 4.91 MPa and improved the wind erosion resistance of the consolidation layer, effectively resisting wind damage. At a construction site, the waterborne polymer was sprayed on soil for backfilling, and the concentration of PM10 was reduced by 67.41%, confirming the effectiveness for large-scale utilization.

Artificial Neural Networks for Modelling and Predicting Urban Air Pollutants: Case of Lithuania

This study focuses on the Vilnius (capital of Lithuania) agglomeration, which is facing the issue of air pollution resulting from the city’s physical expansion. The increased number of industries and vehicles caused an increase in the rate of fuel consumption and pollution in Vilnius, which has rendered air pollution control policies and air pollution management more significant. In this study, the differences in the pollutants’ means were tested using two-sided t-tests. Additionally, a 2-layer artificial neural network and a pollution data were both used as tools for predicting and warning air pollution after loop traffic has taken effect in Vilnius Old Town from July of 2020. Highly accurate data analysis methods provide reliable data for predicting air pollution. According to the validation, the multilayer perceptron network (MLPN1), with a hyperbolic tangent activation function with a 4-4-2 partition, produced valuable results and identified the main pollutants affecting and predicting air quality in the Old Town: maximum concentration of sulphur dioxide per 1 hour (SO2_1 h, normalized importance = 100%); carbon monoxide (CO) was the second pollutant with the highest indication of normalized importance, equalling 59.0%.

Fear in a Handful of Dust: The Epidemiological, Environmental, and Economic Drivers of Death by PM2.5 Pollution

This study evaluates numerous epidemiological, environmental, and economic factors affecting morbidity and mortality from PM2.5 exposure in the 27 member states of the European Union. This form of air pollution inflicts considerable social and economic damage in addition to loss of life and well-being. This study creates and deploys a comprehensive data pipeline. The first step consists of conventional linear models and supervised machine learning alternatives. Those regression methods do more than predict health outcomes in the EU-27 and relate those predictions to independent variables. Linear regression and its machine learning equivalents also inform unsupervised machine learning methods such as clustering and manifold learning. Lower-dimension manifolds of this dataset's feature space reveal the relationship among EU-27 countries and their success (or failure) in managing PM2.5 morbidity and mortality. Principal component analysis informs further interpretation of variables along economic and health-based lines. A nonlinear environmental Kuznets curve may describe the fuller relationship between economic activity and premature death from PM2.5 exposure. The European Union should bridge the historical, cultural, and economic gaps that impair these countries' collective response to PM2.5 pollution.