The country’s advancement is fueled by regional growth. It frequently has many detrimental effects in its application, including contamination. Climate, notably temperature, is negatively impacted by the ensuing pollution. This study uses the Multiple Correspondence Analysis (MCA) method to measure the pollution index, followed by the instrumental variable (IV) method to calculate the effect of development on pollution and temperature. Rural data from Podes 2018 is among the data used in this investigation. The findings of this study show that developed and developing areas are where the negative pollution index forms the most frequently. The construction and the resulting pollution index have a negative impact on temperature. The development process should pay attention to environmental aspects to anticipate worse temperature changes in the coming period.

Air pollution affects public health and the environment, creating great concern in developed and developing countries. In India, there are numerous reasons for air pollution, and festivals like Diwali also contribute to air contamination. Determining the polluted region using several air contaminants is significant and should be analyzed carefully. This study aims to analyze the air quality in Tamil Nadu, India, during the Diwali festival from 2019 to 2021, based on multiple air pollutants. The study models the impact of air pollution as a Multi-Attribute Decision-Making (MADM) problem. It introduces a hybrid approach, namely the Analytical Hierarchy Process-Entropy-VlseKriterijumska Optimizacija I Kompromisno Resenje (AHP-Entropy-VIKOR) model, to analyze and rank the areas based on the quality of air. A combined approach of AHP and entropy is employed to determine the weights of multiple air pollutants. The VIKOR approach ranks the areas and identifies the areas with the worst air quality during the festival. The proposed model is validated by performing the Spearman’s rank correlation with two existing MADM methods: Combinative Distance Based Assessment (CODAS) and Weighted Aggregates Sum Product Assessment (WASPAS). Sensitivity analysis is carried out to assess the effects of the priority weights and the dependency of the pollutants in ranking the regions. The highest air pollution level during the festival was seen in Cellisini Colony (2019), Rayapuram (2020), T. Nagar and Triplicane (2021) in their respective year. The results demonstrate the consistency and efficiency of the proposed approach.

This paper presents a summary of PM2.5, PM10 and gaseous pollutant concentrations measured during each season of the year from March 1, 2018 to February 28, 2019 in Visakhapatnam city (17.6868°N, 83.2185°E) located on the east coast of India. The city is studded with 14 major industries and surrounded on three sides by mountains and the Bay of Bengal on the fourth side. The monthly variations of mass concentrations of PM2.5, PM10 and gaseous pollutants SO2, NO2 and CO recorded revealed the impact of atmospheric pollutants originating from industry, urbanization and increased automobile traffic. The seasonal variability of PM concentrations, highest in winter and lowest in summer, is observed. The annual averages for 2018 in Visakhapatnam are 103.5 ± 55.1 ?g/m3 and 111.5 ± 29.1 ?g/m3 for PM2.5 and PM10 respectively. To establish the causal relationship between PM2.5, PM10 and the gaseous pollutants we used Pearson correlation and regression statistical methods. The Pearson correlation coefficients between PMs and gaseous pollutants were either high or moderate. Regression results further confirmed that NO2 and SO2 significantly impacted PM2.5 and PM10 in Visakhapatnam city.

To study the characteristics of air quality and the relationship between air quality and weather factors, based on daily meteorological data from 2016 to 2019 in Beijing using information diffusion technology, the probability distribution of air quality index in different seasons and the development trend of air quality have been studied, and the relationship between weather factors and air quality discussed. The results show that: 1) According to the air quality, the order of the four seasons is summer, spring, autumn and winter. In summer, the frequency of moderate air pollution and above is about 2.54%, and the frequency of serious air pollution is about 0%. In winter, the frequency of moderate air pollution and above is 17.83%, and the frequency of serious air pollution is 2.93%. 2) The air quality of Beijing has been improving in recent years, which shows that with the strengthening of air pollution control efforts, certain results have been achieved. 3) Quantitative analysis of the relationship between winter air quality index and temperature and wind in Beijing shows that the degree of air pollution in winter increases with the increase of temperature and decreases with the increase of wind force. The frequency of mild air pollution and above is about 8.91% when the daily maximum temperature is below 0°C and 48.78% when the daily maximum temperature is above 9°C. The frequency of mild air pollution and above is about 45.17% when the daily maximum wind force is level 0, and 20.89% when the daily maximum wind force is level 3 and above. Examples show that the information diffusion technology can make full use of the location information of the sample points by transforming the traditional sample data points into fuzzy sets, and achieves good results in frequency statistics and trend fitting. The model established in this paper has the value of popularization and application.

The association between urbanization and health at the global level, as well as the role of air pollution, has increased the interest in studies, aimed to improve the air quality of urban areas. Addressing the challenges of pollution caused by urbanization plays a crucial role in developing sustainable urbanization. Understanding the temporal characteristics of particulate matter mass concentrations with an aerodynamic diameter of less than 2.5 ?m and 10 ?m (PM2.5 and PM10) is very important to counter the effect of air pollution. We have analysed and interpreted the diurnal, monthly and seasonal variations of one-hour average PM concentrations taken from Central Pollution Control Board (CPCB) for six stations over Hyderabad, India during March 2018 to February 2020. Average concentrations of PM2.5 (41.5 ?g/m3) and PM10 (91.52 ?g/m3) for two consecutive years (2018 and 2019) are found to exceed the standard values of World Health Organization (WHO) standards (PM2.5 = 10 ?g/m3 and PM10 = 20 ?g/m3) and National Ambient Air Quality Standards (NAAQS) (PM2.5 = 40 ?g/m3 and PM10 = 60 ?g/m3). A clear diurnal and seasonal variations are observed for all the stations. In diurnal cycle, a large PM concentration was observed between 8 AM to 10 AM and again between 6 PM to 9 PM with a minimum at 3 PM in all seasons and also for all stations which clearly shows semidiurnal variations. Data analysis shows a high concentration of particulate matter in winter compared to other seasons. The PM2.5 (PM10) concentrations in winter were found to be increased by three (two times) when compared to monsoon. The ratio of PM2.5 to PM10 is very close to 0.5 during post-monsoon and winter, and 0.4 in summer and monsoon seasons, which clearly shows that PM2.5 comprises a major portion of PM10. The PM2.5 and PM10 are highly correlated with correlation coefficient 0.9. Out of 6 stations, Zoo Park is contributing more particulate matter pollutant concentrations.

In recent years, cities in southern China have experienced severe air pollution, despite having few sources of pollutants. To study the pollution characteristics of PM2.5 in these ?low industrialized? cities, a numerical method based on the HYSPLIT4 Model and Kriging Spatial Interpolation Technology was established. Simulation results showed that the PM2.5 pollution in Guilin was affected by both internal and external sources. The backward air mass trajectory from July 2017 to June 2018 was simulated using the HYSPLIT model. The cluster analysis results indicated that the direction of trajectory ? accounted for 63.09% of the air pollution in the city. The average concentration of PM2.5 pollution was 45.94 ?g.m-3. The pollutant originated from the ?Xiang-Gui Corridor.? The location of the sources was collocated with high industry regions. The spatial characteristics of the four pollution processes in the winter of 2017 were analyzed using a spatial interpolation method. The results showed that the transport of air masses in the direction of trajectory ? was obstructed by a mountain system in the northeast. Therefore, two air pollution accumulation centers and a topographic weakening zone dominated by internal and external sources were formed. It can be inferred that the air pollution in Guilin is affected by both internal and external factors. These results provide important theoretical and technical support for regional air pollution control and environmental protection.