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Actual acquired air quality time series data are highly volatile and nonstationary, and accurately predicting nonlinear time series data containing complex noise is an ongoing challenge. This paper proposes an air quality prediction method based on empirical mode decomposition (EMD), a transformer and a bidirectional long short-term memory neural network (BiLSTM), which is good at addressing the ultrashort-term prediction of nonlinear time-series data and shows good performance for application to the air quality dataset of Patna, India (6:00 am on October 3, 2015–0:00 pm on July 1, 2020). The AQI sequence is first decomposed into intrinsic mode functions (IMFs) via EMD and subsequently predicted separately via the improved transformer algorithm based on BiLSTM, where linear prediction is performed for IMFs with simple trends. Finally, the predicted values of each IMF are integrated using BiLSTM to obtain the predicted AQI values. This paper predicts the AQI in Patna with a time window of 5 h, and the RMSE, MAE and MAPE are as low as 5.6853, 2.8230 and 2.23%, respectively. Moreover, the scalability of the proposed model is validated on air quality datasets from several other cities, and the results prove that the proposed hybrid model has high performance and broad application prospects in real-time air quality prediction. [ABSTRACT FROM AUTHOR]

Municipal wastewater treatment plants (MWWTPs) are considered essential to protect human health and aquatic systems. However, several studies in the scientific field have identified adverse environmental effects in these treatment units that involve challenges in the water industry. Therefore, this work has the following objectives: (1) to understand the global context in which MWWTPs operate and (2) to determine the thematic and geographic trend in scientific research. Through the "Publish or Perish" software, scientific articles indexed in the Scopus and Google Scholar databases during the period 2000 to 2020 were obtained. Specific terms were used: "Municipal Wastewater Treatment Plants"; "Municipal Wastewater Treatment Plants," so that all terms must be contained in the title of the document. Categorization was defined according to key concepts considered as adverse effects indicated in the literature and that involve common problems in the water industry at the municipal level. As a result, 1844 documents (papers, reviews, books, book chapters, citations, letters, theses, and conference papers) were retrieved and subjected to a process of refinement (discrimination) where only articles, chapters, conference papers, and reviews were considered. Finally, 800 articles were selected for further content analysis, identifying seven categories and 30 subcategories. The category of "Emerging contaminants" was the most relevant within the research topics, and the category with the least available research was "Air quality." The articles were also analyzed by economic region, identifying Asia with the largest number of studies on various topics. In conclusion, future research should be oriented towards management, energy, and air quality issues, because they are subjects of little research involving process efficiency, energy savings, and impacts on the quality of life. [ABSTRACT FROM AUTHOR]

In the backdrop of rapid and continuous deterioration in the quality of air, water and land, pollution prevention strategies have become a critical business issue to the industrial sector throughout the world. The present study aims to explore pollution prevention initiatives and their actual implementation across five different polluting industries from a large developing country's perspective. Ten sample plants (two from each industry group) were chosen from five different polluting industry groups (iron and steel, cement, pulp and paper, leather and pharmaceuticals) of a large developing economy like India. This study reveals that environmental concerns have increasingly spread among the selected polluting industries. Addressing pollution prevention practices into their business functions these industries have become more responsive to modify production process and improve the quality of the environment. However, there is a huge potential to improve it further. [ABSTRACT FROM AUTHOR]

The Omicron pandemic broke out in Shanghai in March 2022, and some infected people spread to some cities in the Yangtze River Delta (YRD) region. To achieve the dynamic zero-COVID target as soon as possible, Shanghai and nine cities that were heavily affected by Shanghai implemented the lockdown measures. This paper aims to quantify the impact of the lockdown on air quality and human health. A difference-in-difference (DID) model was first used to measure the impact of the lockdown on air quality in these ten cities. Based on the results of the DID model, we estimated the PM2.5-related health and economic benefits using the concentration–response function and the value of statistical life method. Results showed that the lockdown has reduced the concentrations of PM2.5, PM10, SO2, NO2, and CO by 9.87 μg/m3, 17.31 μg/m3, 0.75 μg/m3, 9.03 μg/m3, and 0.07 mg/m3, respectively. The number of avoided premature deaths due to PM2.5 reduction was estimated to be 35,342. The resulting economic benefits totaled 18.86 billion US dollars. We investigated the reasons for the air quality improvement in these ten cities and found the "3 + 11" policy has had a great impact on air quality. Compared with the first COVID-19 lockdown in early 2020, the effect of the lockdown in 2022 was smaller. These findings demonstrated that reductions in anthropogenic emissions would achieve substantial air quality improvement and health benefits. This paper re-emphasized continuous efforts to improve air quality are essential to protect public health. [ABSTRACT FROM AUTHOR]

Nitrogen cycling in northern temperate forest ecosystems could change under increasing atmospheric CO2 and tropospheric O3 as a result of quantitative and qualitative changes in plant litter production. At the Aspen Free Air CO2–O3 Enrichment (FACE) experiment, we previously found that greater substrate inputs to soil under elevated CO2 did not alter gross N transformation rates in the first 3 years of the experiment. We hypothesized that greater litter production under elevated CO2 would eventually cause greater gross N transformation rates and that CO2 effects would be nullified by elevated O3. Following our original study, we continued measurement of gross N transformation rates for an additional four years. From 1999 to 2003, gross N mineralization doubled, N immobilization increased 4-fold, but changes in microbial biomass N and soil total N were not detected. We observed year-to-year variation in N transformation rates, which peaked during a period of foliar insect damage. Elevated CO2 caused equivalent increases in gross rates of N mineralization (+34%) and NH immobilization (+36%). These results indicate greater rates of N turnover under elevated CO2, but do not indicate a negative feedback between elevated CO2 and soil N availability. Elevated O3 decreased gross N mineralization (−16%) and had no effect on NH immobilization, indicating reduced N availability under elevated O3. The effects of CO2 and O3 on N mineralization rates were mainly related to changes in litter production, whereas effects on N immobilization were likely influenced by changes in litter chemistry and production. Our findings also indicate that concomitant increases in atmospheric CO2 and O3 could lead to a negative feedback on N availability. [ABSTRACT FROM AUTHOR]

Air pollution poses a great danger to human health and economic development, and understanding people's willingness to pay for improved air quality (WTPA) impacts environmental protection. This paper investigates WTPA based on the perspective of subjective well-being (SWB) and analyzes the mediating role of social interaction on the relationship between the two. This paper distinguishes social interactions into online and offline interactions and analyzes whether the mediating effect of the two different interactions on SWB and WTPA exists separately. Using data from the 2018 China General Social Survey (CGSS), we find that SWB has a significant positive effect on WTPA, individuals with higher well-being have higher pro-environmental willingness; there is no age, education level, sex of the person, or regional heterogeneity in the effect of SWB on WTPA; offline social interactions play a partially mediating role between SWB and WTPA, while online social interactions failed to mediate between the two. This paper's policy implication is that improving residents' subjective well-being is both an important development goal and an essential way to resolve the conflict between economic development and environmental protection. [ABSTRACT FROM AUTHOR]

Air pollution is a risk factor for many diseases that can lead to death. Therefore, it is important to develop forecasting mechanisms that can be used by the authorities, so that they can anticipate measures when high concentrations of certain pollutants are expected in the near future. Machine Learning models, in particular, Deep Learning models, have been widely used to forecast air quality. In this paper we present a comprehensive review of the main contributions in the field during the period 2011–2021. We have searched the main scientific publications databases and, after a careful selection, we have considered a total of 155 papers. The papers are classified in terms of geographical distribution, predicted values, predictor variables, evaluation metrics and Machine Learning model. [ABSTRACT FROM AUTHOR]

Air quality has a significant impact on human health, in order to alleviate the air pollution and improve the ability to predict the air quality. In this paper, a prediction model of air quality index composed of variational mode decomposition and temporal convolutional network was proposed. First, in order to reduce the non-stationarity and randomness of the time series, the original air quality index sequence was decomposed by variational mode decomposition, and the decomposition number was determined by the central frequency method to decompose into multiple relatively stable sub-sequences with different frequency scales. Then, the decomposed sub-stable sequence was predicted by the time convolutional network. Finally, the prediction data were integrated and reconstructed to obtain the final prediction results. Comparing the results of other forecasting models by performance evaluation metrics, the combined forecasting model proposed in this paper reduced RMSE by 20.9%, 19.2%, 5.1%, 29.9%, 23.7% on the Beijing dataset. MAPE reduced by 26.6%, 22.3%, 19.5%, 28.9%, 15.0%, respectively. MAE decreased by 19.1%, 20.6%, 9.6%, 29.5%, 23.5%. R2 increased by 4.6%, 4.0%, 0.8%, 14.9%, 5.5% respectively. This proves the accuracy and reliability of the proposed model. [ABSTRACT FROM AUTHOR]

Air toxics are airborne pollutants known or suspected to cause cancer or other serious health effects, including certain volatile organic compounds (VOCs), prioritized by the US Environmental Protection Agency (EPA). While several EPA-designated air toxics are monitored at a subset of Canadian National Air Pollution Surveillance (NAPS) sites, Canada has no specific 'air toxics' control priorities. Although pulp and paper (P&P) mills are major industrial emitters of air pollutants, few studies quantified the spectrum of air quality exposures. Moreover, most NAPS monitoring sites are in urban centers; in contrast, rural NAPS sites are sparse with few exposure risk records. The objective of this pilot study was to investigate prioritized air toxic ambient VOC concentrations using NAPS hourly emissions data from a rural Pictou, Nova Scotia Kraft P&P town to document concentration levels, and to determine whether these concentrations correlated with wind direction at the NAPS site (located southwest of the mill). Publicly accessible Environment and Climate Change Canada data (VOC concentrations [Granton NAPS ID: 31201] and local meteorological conditions [Caribou Point]) were examined using temporal (2006-2013) and spatial analytic methods. Results revealed several VOCs (1,3-butadiene, benzene, and carbon tetrachloride) routinely exceeded EPA air toxics-associated cancer risk thresholds. 1,3-Butadiene and tetrachloroethylene were significantly higher ( p < 0.05) when prevailing wind direction blew from the northeast and the mill towards the NAPS site. Conversely, when prevailing winds originated from the southwest towards the mill, higher median VOC air toxics concentrations at the NAPS site, except carbon tetrachloride, were not observed. Despite study limitations, this is one of few investigations documenting elevated concentrations of certain VOCs air toxics to be associated with P&P emissions in a community. Findings support the need for more research on the extent to which air toxics emissions exist in P&P towns and contribute to poor health in nearby communities. [ABSTRACT FROM AUTHOR]

The paper presents a specific method of environmental impact assessment applied in Strategic Environmental Assessment (SEA) for the National Emission Reduction Plan (NERP) in the Republic of Serbia, based on air quality. The specificity of the approach is in the application of a semiquantitative method of multicriteria evaluation based on air dispersion modeling and the integration of SEA goals, indicators and criteria for assessing the impact of the NERP on the quality of air and other environmental elements in this method. When predicting changes in air quality for the planning horizon to 2028, the physical, geographical and climatic characteristics of the area were taken into account, as well as technical measures to reduce SO2 emissions, since this was the dominant pollutant from the Serbian coal-fired power plants studied by the NERP. Air pollution modeling was carried out using the AERMOD software package based on the data collected, and the quantitative results obtained were used in a multicriteria evaluation as part of the SEA. The results of the research indicated the importance of applying this approach in order to significantly increase objectivity in the SEA process, since it is an important element of decision making at the strategic level. In addition, a comparative presentation of the modeling results before and after application of the NERP was an important part of the SEA process, and it provided a clear insight into expected changes in the air quality. This is a key argument for making appropriate policy decisions on spatial, energy, environmental and socio-economic development in the Republic of Serbia, which, like other developing countries, is sluggishly following global trends in energy transition. [ABSTRACT FROM AUTHOR]

The overall ecological benefits of green space landscape mainly depend on the internal structure and spatial layout of green space. The study area is six urban districts in Xi'an, with the support of remote sensing technology and ArcGIS, which explored the scale effect and spatial differentiation of the green space landscape patterns and their synergistic influence on PM2.5 and PM10. In this paper, the principal component analysis was used to screen out the indices that can represent the green space landscape pattern in the study area, and on this basis, the landscape metrics weighted change model based on the entropy method was established for the spatial scale analysis of green space landscape. Then, the spatial differentiation characteristics of green space landscape patterns and their impacts on air quality were studied comprehensively using statistical analysis methods. The results show obvious gradient changes in the green space landscape pattern in Xi'an, and the constructed green space landscape pattern metrics can better explain the variation of atmospheric PM concentration. Moreover, we further found that increasing the number and area of green space patches and reducing their overall dispersion can effectively reduce PM concentration. This paper is the first to reveal the characteristic scales of green space landscape patterns in the main urban area of Xi'an. It provides a scientific basis for creating a reasonable spatial distribution pattern of urban green space, which plays an active role in improving the ecological benefits of urban green space. [ABSTRACT FROM AUTHOR]

Affected by the epidemic and other factors, the global economy is in a downturn, and countries around the world are under unprecedented debt pressure. How will this affect environmental protection? Taking China as an example, this paper empirically studies the impact of changes in local government behavior on urban air quality under fiscal pressure. This paper uses the generalized method of moments (GMM) to find that fiscal pressure has significantly reduced PM2.5 emissions, with a unit increase in fiscal pressure will increase PM2.5 by about 2%. The mechanism verification shows that three channels affect PM2.5 emissions: (1) fiscal pressure has prompted local governments to relax the supervision of existing pollution-intensive enterprises. (2) Local governments reduce environmental regulations for attracting more pollution-intensive enterprises. (3) Local governments tend to reduce environmental protection investment to save fiscal expenses. The paper's conclusions provide new policy ideas for promoting environmental protection in China, as well as served as a case for analyzing current changes in environmental protection in other countries. [ABSTRACT FROM AUTHOR]

Papers on population aging and the effects of environmental quality on health expenditure have critical policy consequences. However, findings in the relevant literature are mixed, and papers generally focus on developed countries. To provide new information to the literature, this paper examines the impact of globalization, economic growth, greenhouse gas emissions, and population aging on health expenditures in emerging market economies with annual data for the period 2000 to 2018. The paper follows a second-generation advanced panel data method that considers cross-sectional dependency. The estimation results reveal that population aging, economic growth, and greenhouse gas emissions have an increasing effect on health expenditures, while globalization has a decreasing effect. Furthermore, one-way causality running from population aging to health expenditures is confirmed, while a feedback causality relationship is observed between health expenditures and other indicators (globalization, economic growth, and greenhouse gas emissions). After all, the outputs of this paper can provide critical policy implications about the relationships between aging, globalization, air quality, and health expenditures in developing countries. [ABSTRACT FROM AUTHOR]

Based on a review of the relevant literature, this paper uses data from 70 large and medium-sized cities to test whether the purchasing behaviours of Chinese urban residents are affected by air quality. Despite the indirectness of perceptions of air quality and the non-immediacy of the impact of air quality, buyers' awareness of environmental factors plays an important role in their willingness to pay. This paper proposes that residents' awareness strengthens the influence of air quality on commercial housing prices and conducts empirical tests to examine this proposition. To overcome the deviation caused by the heterogeneity of the samples, the paper also discusses whether the above relationship varies by region, development level and political level. The empirical results show that the average annual sales price of commercial housing decreases by 0.881 units for each unit of air pollution improvement. Comprehensive analysis finds that Chinese urban residents concern the air quality near commercial housing, but the degree of influence is weak. Residents' level of awareness strengthens the influence of air quality on commercial housing prices, but the relationship varies by region, development level and political level. [ABSTRACT FROM AUTHOR]

Based on panel data of 282 prefecture-level and above cities in China from 2013 to 2020, this paper investigates the impact and transmission paths of the "LFD" land disposal model on urban air quality at the theoretical and empirical levels using dynamic fixed-effects and dynamic spatial Durbin models. The results show that the way land is allocated in a city has a lagging and long-term impact on air quality not only locally but also in neighboring cities. The type of land supply by local governments to different sectors is an important pathway to influence urban air quality. Extended analysis shows that land market reforms in China can significantly reduce urban air quality index (AQI) and effectively mitigate urban air quality, with long-term effects. This paper provides a theoretical and scientific basis for correcting the mismatch of land resources and promoting urban ecological environment in China. [ABSTRACT FROM AUTHOR]

Air pollution is a serious problem that affects economic development and people's health, so an efficient and accurate air quality prediction model would help to manage the air pollution problem. In this paper, we build a combined model to accurately predict the AQI based on real AQI data from four cities. First, we use an ARIMA model to fit the linear part of the data and a CNN-LSTM model to fit the non-linear part of the data to avoid the problem of blinding in the CNN-LSTM hyperparameter setting. Then, to avoid the blinding dilemma in the CNN-LSTM hyperparameter setting, we use the Dung Beetle Optimizer algorithm to find the hyperparameters of the CNN-LSTM model, determine the optimal hyperparameters, and check the accuracy of the model. Finally, we compare the proposed model with nine other widely used models. The experimental results show that the model proposed in this paper outperforms the comparison models in terms of root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R2). The RMSE values for the four cities were 7.594, 14.94, 7.841 and 5.496; the MAE values were 5.285, 10.839, 5.12 and 3.77; and the R2 values were 0.989, 0.962, 0.953 and 0.953 respectively. [ABSTRACT FROM AUTHOR]

This paper proposes a clustering approach for multivariate time series with time-varying parameters in a multiway framework. Although clustering techniques based on time series distribution characteristics have been extensively studied, methods based on time-varying parameters have only recently been explored and are missing for multivariate time series. This paper fills the gap by proposing a multiway approach for distribution-based clustering of multivariate time series. To show the validity of the proposed clustering procedure, we provide both a simulation study and an application to real air quality time series data. [ABSTRACT FROM AUTHOR]

The air quality index has a severe effect on the determination of health conditions of a city. The prediction of air quality index can aid in determining the optimum route in case of traffic and it can also aid in determining the pollutants which have severe impact on human health conditions. The paper presents an air quality prediction system using various machine learning based models. The air quality index is determined by measuring the different gases present in the atmosphere. In this paper we have considered seven such parameters as concentration levels of Particulate Matter 2.5 (PM2.5), Particulate Matter 10 (PM10), Carbon Mono oxide (CO), Nitrogen Dioxide (NO2), Ammonia (NH3), Sulphur Dioxide (SO2) and Ozone (O3) levels for the duration between the year January 2019 to October 2023 for a crowded area of Varanasi city. The various pre processing techniques have been used in the dataset for the implementation of machine learning models. The performance of the models have been compared for the prediction of the air quality. The results show that the Random Forest and Decision Tree based model achieves the maximum accuracy of approximately 100% as compared to 98%, 95% and 93% and 79% for the SVM, Multi layer Perceptron network, KNN classification and Linear Regression. [ABSTRACT FROM AUTHOR]

In October 2003 four contiguous prefectures in Greater Tokyo introduced Low Emission Zones (LEZs) from which diesel trucks and buses without particulate filters have been banned from entering. This paper analyzes the effects of this large-scale intervention on air quality, new vehicle registrations, and birthweights. We use a matching approach to construct a control group comparable to the designated areas in terms of propensity scores based on municipality characteristics during the pre-intervention period and apply a difference-in-differences design. We find evidence that the intervention led to reductions in hourly particulate matter concentrations and the incidence of low birthweights in the Greater Tokyo LEZ relative to the control group. We also find that the LEZs led to increases in registrations of new trucks and buses. This is not the case for passenger cars, which were exempt from the regulations. Our paper provides the first evidence of a significant link between LEZs and reduced incidence of low birthweights. [ABSTRACT FROM AUTHOR]

Modeling dispersion of aircraft emissions is challenging because aircraft are mobile sources with varying emissions rates at different elevations depending on the operating mode. Aircraft emissions during landing and take-off cycle (LTO) influence air quality in and around the airport, and depending on the number of aircraft operations and location of the airport, this influence may be significant. AERMOD (v22112) incorporates a variety of conventional source types to characterize the intended emissions source, leaving the question of which conventional source type(s) best characterizes aircraft activities across the four modes of LTO cycle, unanswered. Currently, the publicly released version of FAA's Aviation Environmental Design Tool (version 3e) models aircraft emissions as a set of AREA sources for all flight segments. A research version of AEDT allows users to model aircraft sources—both fixed wing and rotorcraft—as a series of VOLUME sources in AERMOD. However, both source treatments do not account for plume rise of aircraft jet exhaust. This paper compares AERMOD's performance in describing SO2 concentrations associated with airport sources by comparing model results from the two source options during the summer campaign of the Air Quality Source Apportionment study conducted at the Los Angeles International Airport. We conclude that both VOLUME source and AREA treatments overestimate the highest observed SO2 concentrations despite not accounting for background sources. The VOLUME source option reduces this overestimation by using a higher initial plume spread than the AREA option does, and through the inclusion of meander. Our results suggest the need to include the plume rise of jet exhaust when using AERMOD for airport air quality studies. [ABSTRACT FROM AUTHOR]

In China, the local government's "land for development" strategy has led to a large number of urban construction land allocated to the industrial field, which has promoted the rapid development of industry and economy in the short term but also brought serious environmental quality losses. This paper systematically sets out how land misallocation works on urban air quality and employs the spatial Durbin model (SDM) to conduct an empirical analysis on the panel data of 283 China cities at or above the prefecture level. The result shows that, stimulated by financial maximization and political promotion, in order to obtain more fiscal revenue and growth performance, local governments prefer to allocate a large number of urban construction land to industry and related fields, which leads to the underestimation of industrial land price and the misallocation of land resources. Land misallocation has exerted significant inhibiting effects on the air quality of local and their surrounding cities through inhibiting the upgrading of industrial structure. Further analysis reveals that the bigger the city, the lesser the inhibition effects of land misallocation on upgrading of industrial structure and urban air quality and vice versa. The conclusions of this paper can provide a useful reference for local governments to optimize land allocation, promote economic restructuring, and environmental quality upgrading. [ABSTRACT FROM AUTHOR]

With the development of new technologies, particularly Internet of Things (IoT), there has been an increase in the deployment of low-cost air quality monitoring systems. Compared to traditional robust monitoring stations, these systems provide real-time information with higher spatio-temporal resolution. These systems use inexpensive and low-cost sensors, with lower accuracy as compared to robust systems. This fact has raised some concern regarding the quality of the data gathered by the IoT systems, which may compromise the performance of the environmental models. Considering the relevance of the data quality in this scenario, this paper presents a study of the data quality associated with IoT-based air quality monitoring systems. Following a systematic mapping method, and based on existing guidelines to assess data quality in these systems, we have identified the main Data Quality (DQ) dimensions and the corresponding DQ enhancement techniques. After analyzing more than 70 papers, we found that the most common DQ dimensions targeted by the different works are accuracy and precision, which are enhanced by the use of different calibration techniques. Based on our findings, we present a discussion on the challenges that must be addressed in order to improve data quality in IoT-based air quality monitoring systems. [ABSTRACT FROM AUTHOR]

Air quality prediction is considered one of complex problems. This is due to volatility, dynamic nature, and high variability in space and time of particulates and pollutants. Meanwhile, designing an automated model for monitoring and predicting air quality becomes more and more relevant, particularly in urban regions. Air pollution can significantly affect the environment and eventually citizens' health. In this paper, one of the popular machine learning algorithms, the neural network algorithm, is employed to classify different species of air pollutants. To boost the performance of the traditional neural network, the war strategy optimization algorithm tunes the neural network's parameters. The experimental results demonstrate that the proposed optimized neural network based on the war strategy algorithm can accurately classify air pollutant species. [ABSTRACT FROM AUTHOR]

In order to address the problem of air pollution along with the fast development of the economy and an increase of urban population, this paper aims to capture the spatial–temporal correlation in air quality data and improve the prediction accuracy of PM2.5 concentration. Existing models usually neglect the correlation in temporal and spatial dimensions, and the prediction results do not correspond to those in practical applications. We propose a multi-period spatial–temporal graph convolutional network (MST-GCN), in which the time division module fully explores the temporal correlation within the data and builds separate models for different time periods; the spatial–temporal convolution module captures the characteristics of historical data in the time dimension and spatial dimension, in which GCN is used to learn spatial dependencies between stations, and CNN is applied to learn the temporal dependencies of adjacent time points; meanwhile, a spatial–temporal attention mechanism module is introduced to enhance the key information in different dimensions, thereby capturing the spatial–temporal dynamic correlation. Finally, the experiments with real air quality data from Beijing monitoring stations show that the time division method is effective to extract the features in time dimensions; therefore, the model is able to capture the characteristics and dynamic correlation of air quality data in spatial–temporal dimensions. The superiority of the proposed method has been validated over state-of-the-art methods, and the average mean absolute error and root-mean-square error of MST-GCN are 34.37 and 35.55, respectively, which achieves the best performance in the experiments. [ABSTRACT FROM AUTHOR]

Air quality is an important factor affecting human health. This paper aims to explore the key determinants of urban air quality in China and their synergistic mechanism based on a hybrid model integrating weighted gray relation analysis (WGRA) and qualitative comparative analysis (QCA). First, text mining technology is introduced to excavate factors related to urban air quality, and WGRA method is employed to determine the key influencing factors. Second, taking 42 representative cities in China as samples to apply QCA method, we attempt to investigate the complex causal mechanism between key determinants of urban air quality and air quality index (AQI). Results show that: (1) Since the consistency level of a single conditional variable is not greater than 0.9, a single factor that influences urban air quality cannot constitute the necessary condition to worsen or improve the urban air quality. (2) Synergistic effect mechanisms of key factors influencing urban air quality are obtained, including four configuration paths that bring about high AQI and four configuration paths that inhibit the generation of high AQI. [ABSTRACT FROM AUTHOR]

This paper studies the relationship between the coal-to-gas program and air pollution in China and provides micro-evidence of the mechanism from the perspective of households. Using daily air pollution and meteorological data between January 1, 2016, and January 1, 2020, we assessed the effect of the coal-to-gas program on air pollution by introducing the regression discontinuity designs in time (RDiT). We found that the coal-to-gas program significantly improved air quality and brought significant economic benefits. In the short term, the coal-to-gas program can lead to more than 10 units of reduction in SO2, PM2.5, and AQI in the treatment group, while it can lead to more than 50 units in the long term. Using the difference-in-differences approach, we found that the coal-to-gas program has significantly reduced air pollution. Combined with the micro-panel data of the China Health and Retirement Longitudinal Study from 2011 to 2018, we found that the coal-to-gas program changes the household heating energy choices and that the probability of coal-fired heating of households in pilot areas is decreasing. The study suggests that non-clean energy in households should be further replaced to continue improving air quality. [ABSTRACT FROM AUTHOR]

To cope with the coronavirus disease (COVID-19), national or sub-national regions have carried out many powerful anti-pandemic measures such as locking down, which may improve their regional air quality. This paper examines the relation between regional air pollution and work resumption from a novel post-pandemic perspective. Using a unique panel dataset on China's detailed industrial electricity consumption, this paper does not find a positive relation between post-COVID-19 work resumption and regional air pollution during China's early-stage recovery. This result is obtained after controlling for province and date fixed effects, as well as local weather conditions. However, the positive relations are found in a particular sub-sample of large industrial enterprises and a particular sub-sample of April. These findings indicate that large industrial enterprises may recover first, and the resumption is progressing gradually. Finally, several policy implications are provided, which are essentially helpful for other countries' post-pandemic recovery. [ABSTRACT FROM AUTHOR]

The impact of air pollution is one of the hotspots attracting continuous scholarly attention, but the comprehensive statistical and visual analysis reviews are few. Employing the method of bibliometric analysis, this paper took the relevant literature from 1996 to April 2022 on the Web of Science as the research object. Through the methods of keyword co-occurrence analysis and burst analysis, the spatiotemporal evolution trend, cooperation network, outstanding scholars, knowledge base, and research focus of air pollution impact research are visually presented. Via constructing a common word matrix of high-frequency words, clustering analysis is used to aggregate high-frequency keywords into 24 clusters. By the strategic coordinate analysis method, the relationships within and between clusters were revealed. The main findings include (1) research on the impact of air pollution mainly focusing on human health; (2) the six keywords with the highest centrality are California, hydrocarbons, dioxide, generation, Asia, and diesel; (3) these 11 clusters may be developed into future research hotspots: particulate matter, nitrogen oxides, mortality, ozone, pollution, air quality, asthma, children, epidemiology, aerosols, and polycyclic aromatic hydrocarbons; and (4) seven topics are research hot: daily mortality, long-term exposure, coronary heart disease, concentration, North China plain, traffic-related air pollution, and air pollution. [ABSTRACT FROM AUTHOR]

Air quality prediction technology can provide effective technical means for environmental governance. In recent years, due to the strong nonlinearity of data, there has been extensive research on data analysis and preprocessing techniques. This paper aims to comprehensively summarize and analyze the methods used in air quality forecasting, specifically focusing on four categories: data decomposition, dimensionality reduction, data correction, and spatial interpolation. Each method's purpose, characteristics, improvements, and implementation details are described in detail. The evaluation of data preprocessing methods is based on popularity, accuracy improvements, time consumption, maturity, and implementation difficulty. Among the existing methods, data decomposition and feature selection are commonly used and well-developed. However, outlier detection and spatial interpolation have limited applications and require further research. Furthermore, this paper discusses current challenges in applying these methods and future development trends, providing a valuable reference for future research. [ABSTRACT FROM AUTHOR]

Excellent air quality is important for China to achieve high quality economic development. The paper analyses the spatial and temporal distribution characteristics of the air quality index (AQI) in 288 Chinese cities, and further investigates the driving factors affecting air quality using the spatial Durbin model (SDM) based on the panel data of 288 Chinese cities from 2014 to 2021. The results of the study show that: (1) China’s air quality level has improved in general, but there are large differences in air quality between regions; (2) China’s AQI has significant spatial positive autocorrelation, and the Moran’s scatter plot shows a high–high and low–low agglomeration; (3) The driving factors of air quality have different effects, and regional heterogeneity is obvious. Some developed regions in China have already crossed the inflexion point of the environmental Kuznets curve (EKC); promoting industrial upgrading and reducing pollutant emissions can significantly improve urban PM2.5 concentrations; and the “Three-Year Strategy for Conquering the Blue Sky War” policy has lowered the AQI in North China and improved PM2.5 concentrations nationwide. Based on the above findings, the paper puts forward corresponding policy recommendations. [ABSTRACT FROM AUTHOR]

Accurately predicting stock prices is crucial for reducing investment-related risks in decision-making. Contemporary challenges to financial behavior, posed by environmental issues such as pollution and climate change, have received limited attention in existing studies on capital market predictability. This paper focuses on the Shanghai Stock Exchange Composite Index (SSEC) and employs air quality and weather data from the Shanghai area as input variables. Subsequently, a hybrid prediction model is constructed by integrating Grey Relational Analysis (GRA), Wavelet Decomposition (WD), and Bidirectional Long Short-Term Memory (BiLSTM) neural networks. The objective is to achieve precise predictions of closing prices. Additionally, this study validates the feasibility of incorporating environmental factors as input variables for stock price prediction, using the Shenzhen Component Index (SZI) and Hang Seng Index (HSI) as case studies, while also assessing the applicability of the GRA-WD-BiLSTM model. The findings demonstrate that the GRA-WD-BiLSTM model exhibits superior applicability and prediction performance in stock price forecasting, with respective prediction accuracies of 95.93%, 93.02%, and 97.07% when accounting for environmental factors. The incorporation of GRA and WD contributes to enhancing single models' performance while integrating air quality and weather factors, which prove valuable in accurately predicting stock prices. The findings also indicate that the impact of regional environmental factors on local stock exchange index prices shows variability. [ABSTRACT FROM AUTHOR]

This paper aims to assess the impact of restrictive measures against the COVID-19 spread on the air quality of the most representative urban centers in Italy during the 66 days of the first lockdown, integrating a broad and detailed set of socioeconomic and health data into machine learning techniques and correlation analysis. Hierarchical Clustering analysis applied to all 104 Italian provinces indicated a group of six provinces to represent the urban environment in Italy. In contrast, correlation analyses suggested two meteorological parameters and four other air quality parameters as the most skilful at expressing changes in air quality during the first lockdown. Filtering the effects of seasonality, NO concentrations were the ones that most acted in improving urban air quality, showing reductions of up to 48% in all analyzed provinces, directly related to reductions in population mobility in this period (other studies reported an incisive role of pollutants as N O 2 and P M 10 or P M 2.5 in the SARS-CoV-3 spread). However, there were increases in P M 10 concentrations related to the use of wood burning for heating, and in S O 2 concentrations associated with the food industry, a sector slightly affected by the restrictive measures for being framed as essential. Naples was the only province which reported concentration reductions in all pollutants evaluated, including ozone (7%). However, it was the one that registered the most significant increases during the first days after the lockdown, probably due to the less restrictive measures applied to provinces with the lowest contamination numbers. [ABSTRACT FROM AUTHOR]

Public investments in subway systems are often motivated by improving local air quality. Recent studies, however, have reached different conclusions on the air quality benefits of subway investment. To reconcile these findings, this paper examines the air quality effects of all 359 subway line openings in China between 2013 and 2018. The machine learning method adopted in this paper substantially improves the consistency and precision of the estimates by purging seasonality, volatility, and the nonlinear effects of meteorological conditions in air quality data. The empirical results suggest an insignificant short-term effect and a significant long-term effect, which is expected as the adjustment of commuting mode takes time. Using the causal forest approach, the heterogeneity analysis find that a city that is experiencing rapid economic growth from a lower income level and currently has fewer subway lines is more likely to experience statistically significant improvements in air quality from a subway opening. These findings help reconcile the different findings in the literature and shed light on air pollution reduction as one of the objectives of public transit investment. [ABSTRACT FROM AUTHOR]

Every year the concern from environmental pollution increases as it affects human health. In the current paper, it is analyzed the evolution of the air quality in different cities on international level related to the reduction in mobility trend due to the emergence of the pandemic. The air pollution is studied in order to achieve healthier cities in the future and create an awareness for the citizens. The COVID-19 pandemic situation was declared during the first months of 2020 and has shown (indirectly) the effects that restrictions on human activity and road traffic have on atmospheric pollutant values. This pandemic condition derived, in most cases, in mobility restrictions in order to decrease the spread of the virus through the density of population in different places and, as a result of these restrictions, pollutant presence decreased equally. This article analyzes some of the largest cities around the world, such as Tsuen Wan district in Hong Kong (China), Los Angeles (USA), London (UK), São Paulo (Brazil), Bangalore (India), Johannesburg (South Africa), and Sydney (Australia) in order to check the different mobility restriction policies established in each location, and how those restrictions led to decrease the levels of some pollutant particles such as NO2 in the atmosphere. This should serve as a focus of awareness of the city and political interests of how it affects human activity, and those measures should be taken to reduce pollutants. [ABSTRACT FROM AUTHOR]

Although green technology innovation is an important means to balance the environment and economy, few studies have analyzed the employment effects of green technology innovation in developing countries. Therefore, this paper employs the panel data of 286 prefecture-level cities in China from 2006 to 2017 to empirically test the impact of various green technology innovations on urban employment. Meanwhile, from the perspectives of air quality and industrial structure optimization, the influencing mechanism of green technology innovation on employment is deeply analyzed. The results reveal that all kinds of green technology innovation can significantly increase the employment level of the city, and the promotion effect is strongest in the 2nd year after the patent application. According to the results of heterogeneity analysis, key cities of environmental protection and non-resource cities are more likely to generate positive employment effects through green technology innovation. Finally, the mediation effect test model confirms that green technology innovation can promote employment by improving air quality and causing industrial structure optimization. [ABSTRACT FROM AUTHOR]

The probabilistic linguistic term set (PLTS) shows great superiority in expressing decision-makers' opinions. The multi-attribute decision-making (MADM) problem under a PLTS environment has gained attention from numerous scholars. However, the majority of current studies are not precise enough in capturing information on PLTS. To address this problem, this paper presents a preference ranking organization method for enrichment of evaluations (PROMETHEE) based on the redefined PLTS and novel score function to solve MADM problems under a PLTS environment. First, an asymmetric normalized PLTS based on prospect theory (ANPLTSPT) is developed. Compared with the PLTS, ANPLTSPT offers a more realistic portrayal of decision-makers' psychological state while ensuring the superiority of the PLTS. Second, regarding the structural complexity of ANPLTSPT, this paper attempts to simplify the computational process through a score function that can embody the characteristics of ANPLTSPT. Inspired by previously formulated score functions, a novel score function called Score-InInHe is developed, the corresponding definitions are given, and some further properties are discussed. With the support of the proposed Score-InInHe, the total score entropy is defined and an objective method to determine the attribute weights is proposed. Finally, the proposed approach is applied to the selection of a green supplier and the determination of air quality. The validity and realistic applicability of the proposed approach are demonstrated through comparative analyses and discussions. [ABSTRACT FROM AUTHOR]

The rapid rise of artificial intelligence (AI) technology has revolutionized numerous fields, with its applications spanning finance, engineering, healthcare, and more. In recent years, AI's potential in addressing environmental concerns has garnered significant attention. This review paper provides a comprehensive exploration of the impact that AI has on addressing and mitigating critical environmental concerns. In the backdrop of AI's remarkable advancement across diverse disciplines, this study is dedicated to uncovering its transformative potential in the realm of environmental monitoring. The paper initiates by tracing the evolutionary trajectory of AI technologies and delving into the underlying design principles that have catalysed its rapid progression. Subsequently, it delves deeply into the nuanced realm of AI applications in the analysis of remote sensing imagery. This includes an intricate breakdown of challenges and solutions in per-pixel analysis, object detection, shape interpretation, texture evaluation, and semantic understanding. The crux of the review revolves around AI's pivotal role in environmental control, examining its specific implementations in wastewater treatment and solid waste management. Moreover, the study accentuates the significance of AI-driven early-warning systems, empowering proactive responses to environmental threats. Through a meticulous analysis, the review underscores AI's unparalleled capacity to enhance accuracy, adaptability, and real-time decision-making, effectively positioning it as a cornerstone in shaping a sustainable and resilient future for environmental monitoring and preservation. [ABSTRACT FROM AUTHOR]

Protecting people from air pollution is an important task for developing countries. Over the past several decades, different types of policies have been enacted in developing countries to improve air quality, with policy impact analysis being conducted as well. As for China, while there is some existing literature on the impact of short-term action on air quality, there is much less research to investigate the roles of long-term air quality control mechanism on air quality change. This paper uses the "2 + 26" city cluster in China as a case study area to particularly analyze the roles played by short-term campaigns that compose the Winter Action Plan and a long-term mechanism referred to as the environmental accountability system in pollution control. Based on econometric and descriptive statistical analyses, it is found that the implementation of the Winter Action Plan has significantly improved the air quality, and the environmental accountability system seems to have exerted an impact, as cities with better air quality appear to establish a more stringent environmental accountability system. The paper provides the following insights for other developing countries: short-term campaigns can quickly reduce pollution and protect human health, while long-term mechanisms are needed to ensure sustainable environmental protection. [ABSTRACT FROM AUTHOR]

In this paper, an interval Air Quality Index (AQI) combination prediction model based on EEMD, VMD, and the weighted power average (WPA) operator is proposed. EEMD and VMD decompose complex AQI data effectively, while WPA operator reasonably aggregates the prediction results of different models. We validate the effectiveness of the proposed model using Shenzhen's daily interval AQI. Furthermore, three kinds of prediction models are compared with the proposed model to highlight its advantages from various perspectives. The results show that the introduction of data decomposition methods significantly improves the model's prediction accuracy, WPA operator further enhances the model's prediction capability, and the incorporation of EEMD and VMD enables the proposed model to have stronger feature extraction capabilities for complex time series. As a result, the model proposed in this paper demonstrates strong generalization ability and prediction accuracy, making it applicable not only for air quality prediction but also for other domains such as economics and environment. [ABSTRACT FROM AUTHOR]

Situated at the intersection between environmental entrepreneurship and urban sustainability, our paper seeks to investigate the links between city-level green venture ecosystems and the ability of urban centres to reduce air pollution. Using a large dataset of 12,834 urban centres from around the world and their associated yearly average particulate matter (PM2.5), we show that an increase in the cumulative number of green start-ups drives the lowering of PM2.5 levels. Looking closely at the subsectors that drive the results, we observe that the urban centres which hosted increased numbers of innovators in smart grid technologies, energy efficiency and wind energy generation (the low carbon energy sector overall) also experienced a decrease in air pollution over the 2010–2019 period. Thus, our study is a global analysis of the environmental impact of green entrepreneurship on local air pollution. Plain English Summary: This research seeks to understand the link between green venture-backed start-ups and air pollution within urban centres around the world. We find that there is a direct link between the number of green start-ups, venture capital (VC) funding and air quality. The air quality of an urban area improves with the number of green start-ups and with the number of VC funding. We use multiple tests and the passing of legislations to determine the causality of that relationship. We also run an analysis to determine the start-up sectors that have the highest impact on the air quality of their urban area, they were energy efficiency, materials, smart grid and wind start-ups. Surprisingly, some of the sectors from which we would have expected the highest impact, such as air quality start-ups, did not improve the quality of the air in their urban centres. [ABSTRACT FROM AUTHOR]

Open waste burning is a significant contributor to air pollution in urban cities. The contribution includes sources from local and centralized waste burning. This paper focuses on the air quality changes in the surrounding regions of the Bhalswa landfill in the north-west part of Delhi, India, where a fire broke out accidentally in the landfill from 20 April 2022 to 8 May 2022. Bhalswa landfill is one among the three major landfills in Delhi, and during the waste burning, PM2.5 shows a maximum average concentration of about 107.32 and 338 µg/m3 for PM10. The average PM2.5 concentration increment was around 45–55%, and for PM10, it was 40–50% during the fire. Apart from particulate matter, fumes generated during open waste burning emit gaseous and toxic substances. Dispersion of pollutants was less during low (< 3 m/s) and calm winds (< 1 m/s) and more during high winds (> 3 m/s). The correlation value (R2) of gaseous pollutants with PM2.5 was significant and exhibited a moderate correlation (R2 around 0.5–0.6). It is high time for residents, government authorities and various other stack holders to be aware and implement policies and adopt technologies for segregation and processing. Identifying potential reuse of waste in energy and volume reduction forms should be encouraged to avoid further accidents/incidents related to open waste burning. The study's outcomes indicate the potential changes that occurred in regional air quality during the open waste burning in a short period. [ABSTRACT FROM AUTHOR]

The quality of air is closely linked with the life quality of humans, plantations, and wildlife. It needs to be monitored and preserved continuously. Transportations, industries, construction sites, generators, fireworks, and waste burning have a major percentage in degrading the air quality. These sources are required to be used in a safe and controlled manner. Using traditional laboratory analysis or installing bulk and expensive models every few miles is no longer efficient. Smart devices are needed for collecting and analyzing air data. The quality of air depends on various factors, including location, traffic, and time. Recent researches are using machine learning algorithms, big data technologies, and the Internet of Things to propose a stable and efficient model for the stated purpose. This review paper focuses on studying and compiling recent research in this field and emphasizes the Data sources, Monitoring, and Forecasting models. The main objective of this paper is to provide the astuteness of the researches happening to improve the various aspects of air polluting models. Further, it casts light on the various research issues and challenges also. [ABSTRACT FROM AUTHOR]

This paper examines whether the SAH orders, implemented in the USA from mid-March to late May 2020, improved air quality in the northeastern states. The estimates are based on panel data from the Environmental Protection Agency and an identification strategy that exploits the exogenous variation in the timing of the SAH orders. We find that the SAH orders reduced the concentrations of the air pollutants nitrogen dioxide ( NO 2 ) and carbon monoxide (CO), whose dominant source is motor vehicle emissions, by approximately 24% and 13%, respectively. The effects were larger for areas of high population density and areas near major roads. We also find that the reductions got smaller, and air pollution gradually approached normal levels, after the orders were lifted. This suggests that the air quality improvements were temporary. [ABSTRACT FROM AUTHOR]

With rapid urbanization, the economic agglomeration within cities is associated with severe air pollution. Urban spatial structure adjustment has been recognized as an effective strategy for improving air quality. However, the research on how to mitigate air pollution originating from economic agglomeration through urban spatial structure adjustment is unclear. Therefore, based on panel data for municipal cities in the Yangtze River Delta (YRD) region during 2008–2018, this study empirically tests the transmission mechanisms among economic agglomeration, urban spatial structure, and air pollution. We use the combination of the social network analysis (SNA) and two-stage least squares (2SLS) methods to verify the effect of economic agglomeration on air pollution. Economic agglomeration's indirect effect on air pollution through urban spatial structure is further tested using mediating effect model and cross-section comparisons. When exploiting an exogenous order rank of node city importance for instrument variable (IV), our finding shows that increasing economic agglomeration by 10% increases air pollution by 12%. In addition, in market forces, monocentricity brings about economic agglomeration's pollution effect, while polycentricity leads to agglomeration's environmental benefits improvement. However, a government-led exogenous polycentricity greatly mitigates economic agglomeration's pollution effect, while in cities with monocentricity, agglomeration slightly increases air pollution. Compared with market power, our paper stresses government intervention in promoting urban spatial structure in terms of polycentric development could be more helpful for improving agglomeration's environmental benefits in China's YRD region. [ABSTRACT FROM AUTHOR]

Although numerous spatiotemporal approaches have been presented to address the problem of missing spatiotemporal data, there are still limitations in concurrently capturing the underlying spatiotemporal dependence of spatiotemporal graph data. Furthermore, most imputation methods miss the hidden dynamic connection associations that exist between graph nodes over time. To address the aforementioned spatiotemporal data imputation challenge, we present an attention-based message passing and dynamic graph convolution network (ADGCN). Specifically, this paper uses attention mechanisms to unify temporal and spatial continuity and aggregate node neighbor information in multiple directions. Furthermore, a dynamic graph convolution module is designed to capture constantly changing spatial correlations in sensors utilizing a new dynamic graph generation method with gating to transmit node information. Extensive imputation tests in the air quality and traffic flow domains were carried out on four real missing data sets. Experiments show that the ADGCN outperforms the state-of-the-art baseline. [ABSTRACT FROM AUTHOR]

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Assessing public preferences for air pollution control is essential to achieving effective air quality improvement, but the internal psychological factors affecting public preferences, especially psychological distance (PD), have only received limited attention. Therefore, this paper explores the role of PD in assessing public preferences for air quality improvement policies. Compared with previous studies that consider psychological factors in the choice model, this study incorporates PD into the choice model as a latent variable by considering both individual responses to measurement questions and socio-economic characteristics in the integrated choice and latent variable model. The results of this study clearly show that PD significantly affects public preferences for air quality improvement policies. Respondents with close PD had obvious preferences for air quality improvement, while those with distant PD were satisfied with the current situation and reluctant to improve it. After considering PD in the analysis, respondents' willingness to pay for one-unit level change of attributes "heavily polluted days," "good air days," "mortality," and "policy postponement" were respectively 10.3791CNY, 10.9005CNY, 11.0427CNY, 28.3412CNY per year. In addition, the paper also found men and respondents with lower levels of education and higher monthly incomes tended to view air pollution as psychologically distant and thus less willing to improve air quality. It is suggested that policy makers should reduce the PD of air pollution among these people by increasing publicity about the hazards of air pollution. This study not only contributes to the literature on the importance of PD in assessing individual preferences, but also provides constructive guidance for policy makers to assess the public's acceptability of air quality improvement. [ABSTRACT FROM AUTHOR]

Foreign direct investment and environmental pollution have been a prominent topic in China's economic development. Therefore, this paper investigates the relationship between foreign direct investment and environmental pollution, and makes practical policy recommendations to support the harmonious development of economy and environment based on the study findings. In this paper, the spatial econometric approach framework with the spatial weight matrix of the Delaunay method is used to examine the influence of foreign direct investment on the level of urban air quality. It uses air quality index as an indicator to measure air pollution comprehensively. Based on the spatial autocorrelation with the aggregation and radiation effects of foreign direct investment, the results show that the distribution of air pollution has considerable spatial impact in terms of spatial nonequilibrium. Through the empirical analysis, our finding reveals that the increase in GDP, the share of secondary industry, and population growth are the important elements affecting the change in air quality. [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]

The Covid-19 pandemic has negatively disrupted the way our economy and society functions. Nonetheless, there have also been some positive externalities of the pandemic on the environment. This paper aims to evaluate the concentration of nitrogen dioxide in Brazilian metropolitan regions after the policies adopted to confront Covid-19. In terms of methodological approach, the study employs cross-sectional quantitative analyses to compare the period of 36 days, i.e., 12 March to 16 April—before (in 2019) and after (in 2020) the pandemic declaration. The data were obtained from the Sentinel 5-P low-Earth polar satellite concerning Brazilian metropolitan regions (n = 24). Thorough spatial and statistical analyses were undertaken to identify the pre- and during pandemic nitrogen dioxide concentrations. Complementarily, Spearman's correlation test was performed with variables that impact air quality. The study results a fall in nitrogen dioxide concentration levels in 21 of the 24 metropolitan regions which was observed. The Spearman's correlation coefficient between the nitrogen dioxide variation and the vehicle density was 0.485, at a significance level of 0.05. With these findings in mind, the paper advocates that while the pandemic has a significant negative consequence on the health of population globally, a series of measures that result in a new social organization directly interfere in the reduction of air pollution that contributes to the quality of the air we breathe. [ABSTRACT FROM AUTHOR]