Search

The Upper Hunter Valley is a major coal mining area in New South Wales (NSW), Australia. Due to the ongoing increase in mining activities, PM10 (air-borne particles with an aerodynamic diameter less than 10 micrometres) pollution has become a major air quality concern in local communities. The present study was initiated to quantitatively examine the spatial and temporal variability of PM10 pollution in the region. An earlier paper of this study identified two air quality subregions in the valley. This paper aims to provide a holistic summarisation of the relationships between elevated PM10 pollution in two subregions and the local- and synoptic-scale meteorological conditions for spring and summer, when PM10 pollution is relatively high. A catalogue of twelve synoptic types and a set of six local meteorological patterns were quantitatively derived and linked to each other using the self-organising map (SOM) technique. The complex meteorology–air pollution relationships were visualised and interpreted on the SOM planes for two representative locations. It was found that the influence of local meteorological patterns differed significantly for mean PM10 levels vs. the occurrence of elevated pollution events and between air quality subregions. In contrast, synoptic types showed generally similar relationships with mean vs. elevated PM10 pollution in the valley. Two local meteorological patterns, the hot–dry–northwesterly wind conditions and the hot–dry–calm conditions, were found to be the most PM10 pollution conducive in the valley when combined with a set of synoptic counterparts. These synoptic types are featured with the influence of an eastward migrating continental high-pressure system and westerly troughs, or a ridge extending northwest towards coastal northern NSW or southern Queensland from the Tasman Sea. The method and results can be used in air quality research for other locations of NSW, or similar regions elsewhere. [ABSTRACT FROM AUTHOR]

Non-exhaust road transport emissions in cities contribute to poor air quality and have an impact on human health. This paper presents a new study of particles emitted by tyre wear in real driving conditions and gives their emission factors. The most frequently emitted particles were collected in urban, suburban and road areas. They were identified and analysed physically and chemically. Their level of toxicity is well known. An overall analysis of the measured pollutants was carried out to assess their emission factors in real driving situations. The highest emitting pollutants, considered separately, were found to have high emission factors. The values obtained exceed the Euro standard for vehicles but are below those of vehicles not equipped with particle filters. Significant test analysis confirmed that the inertia of chemical pollutants is homogeneous. Emission factors have also been provided for PM10 and PM2.5. These results should contribute to the emergence of future regulations of non-exhaust emissions and should help to analyse the exposure-impact relationship for particles from tyre wear. [ABSTRACT FROM AUTHOR]

Air quality in school environments is of particular interest due to the significant amount of time children spend in these settings. Children, being a particularly sensitive demographic, are exposed to various pollutants at school or kindergarten. In this regard, our studies have focused on monitoring the concentrations of three main categories of pollutants: VOCs (volatile organic compounds), VICs and PM (particulate matter). We conducted two experimental campaigns in seven classrooms within public educational institutions. The average concentration values of TVOC (total volatile organic compounds) ranged from 554 µg/m3 to 2518 µg/m3, of CO2 from 1055 ppm to 2050 ppm, of NH₃ (Ammonia) from 843.2 µg/m3 to 1403.4 µg/m3, of PM2.5 from 25.1 µg/m3 to 89.9 µg/m3, and of PM10 from 63.7 µg/m3 to 307.4 µg/m3. In most instances, the registered values exceeded the limit values set by national or international regulations. Furthermore, this study highlights the significant impact of a heat recovery ventilation system in improving indoor air quality by substantially reducing the levels of CO2 and PM. However, it also underscores the need for further measures to more efficiently reduce TVOC concentrations. The aim of our paper was to enhance the understanding of pollution levels in school environments, increase awareness of the importance of indoor air quality, and highlight the adverse effects of polluted air on the health of occupants. [ABSTRACT FROM AUTHOR]

Trajectory ensemble receptor models (TERMs) were widely used to determine the likely source locations and apportionment of air pollutants. This paper describes the development and applications of the Trajectory-ensemble Potential Source Apportionment Web application (TraPSA-Web), a comprehensive toolkit for likely atmospheric pollutant source location apportionments using TERMs and back trajectories generated with the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model. The TERMs integrated within the TraPSA-web include Concentration Field Analysis (CFA), Concentration Weighted Trajectory (CWT), single-site and multiple-site Potential Source Contribution Function (PSCF), and Simplified Quantitative Transport Bias Analysis (SQBA). TraPSA-Web is designed as a web application with a user-friendly modern graphical user interface (GUI), which largely enhances the accessibility to the users. TraPSA-Web will provide the air quality research community with a sophisticated toolkit for (1) easy management of the research project and datasets, (2) efficient automatization for HYSPLIT configurations, calculations, and result aggregations, (3) flexible configurations for the research scenarios and TERM parameters, and (4) interactive visualizations for the pollutant pattern analysis and TERM result mapping. [ABSTRACT FROM AUTHOR]

In today's urban environments, accurately measuring and forecasting air pollution is crucial for combating the effects of pollution. Machine learning (ML) is now a go-to method for making detailed predictions about air pollution levels in cities. In this study, we dive into how air pollution in urban settings is measured and predicted. Using the PRISMA methodology, we chose relevant studies from well-known databases such as PubMed, Springer, IEEE, MDPI, and Elsevier. We then looked closely at these papers to see how they use ML algorithms, models, and statistical approaches to measure and predict common urban air pollutants. After a detailed review, we narrowed our selection to 30 papers that fit our research goals best. We share our findings through a thorough comparison of these papers, shedding light on the most frequently predicted air pollutants, the ML models chosen for these predictions, and which ones work best for determining city air quality. We also take a look at Skopje, North Macedonia's capital, as an example of a city still working on its air pollution measuring and prediction systems. In conclusion, there are solid methods out there for air pollution measurement and prediction. Technological hurdles are no longer a major obstacle, meaning decision-makers have ready-to-use solutions to help tackle the issue of air pollution. [ABSTRACT FROM AUTHOR]

Imported air pollution has a significant impact on urban air quality. Relevant studies have shown that many urban air pollution events are not resourced by local emissions but are imported by air pollution from surrounding areas transported across regions. The prevention and control of air pollution is very necessary. However, the existing supervision of urban air quality mostly relies on ground monitoring stations, which are extremely limited in time and space, and cannot satisfy continuous time-space air pollution research. Therefore, aiming at the problem of urban air pollution control, this paper used MERRA-2 reanalysis data and ground monitoring data to establish a "Time-Longitude-Latitude" three-dimensional pollution curve, and then a genetic algorithm was used to optimize its fitting. This study finally reconstructed the imported air pollution transmission route. This paper takes an air pollution event that occurred in Xuzhou City, China, on 12 January 2020, as an example. Through the analysis of aerosol optical depth (AOD), particulate matter (PM), wind speed, and other factors, we found the source, transmission route, and impact time of this pollution. We have verified the correctness and accuracy of the reconstructed contamination transport paths. It is proved that the method is universal and it can quickly and accurately restore the air pollution transmission route and identify the urban imported air pollution transmission entrance. This method will also provide strong data support for the division of responsibilities of environmental protection departments in various regions for severe air pollution transmission events and provide effective governance ideas for the prevention and control of imported air pollution in recipient cities. [ABSTRACT FROM AUTHOR]

The atmospheric environment is of great importance to human health. However, its influencing factors are complex and variable. An efficient technique is required to more precisely estimate PM2.5 concentration values. In this paper, an enhanced Sparrow Search Algorithm (LASSA)-optimized Light Gradient Boosting Machine (LightGBM) is proposed for PM2.5 concentration prediction. This approach can provide accurate predictions while also reducing potential losses resulting from unexpected events. LightGBM is regarded as an outstanding machine learning approach; however, it includes hyperparameters that must be optimally mixed in order to achieve the desired results. We update the Sparrow Search Algorithm (SSA) and utilize it to identify the optimal combination of the most crucial parameters, using cross-validation to increase the reliability. Using limited air quality data and meteorological data as inputs, PM2.5 concentration values were predicted. The LASSA-LGB's output was compared to normal LGB, SSA-LGB and ISSA-LGB. The findings demonstrate that LASSA-LGB outperforms the other models in terms of prediction accuracy. The RMSE and MAPE error indices were lowered from 3% to 16%. The concordance correlation coefficient is not less than 0.91, and the R2 reached 0.96. This indicates that the proposed model has potential advantages in the field of PM2.5 concentration prediction. [ABSTRACT FROM AUTHOR]

With rapid urbanization, hazardous environmental exposures such as air, noise, plastic, soil and water pollution have emerged as a major threat to urban health. Recent studies show that 9 out of 10 people worldwide breathe contaminated air contributing to over 7 million premature deaths annually. Internet of Things (IoT) and Artificial Intelligence (AI)-based environmental sensing and modelling systems have potential for contributing low-cost and effective solutions by providing timely data and insights to inform mitigation and management actions. While low and middleincome countries are among those most affected by environmental health risks, the appropriateness and deployment of IoT and AI systems in low-resource settings is least understood. Motivated by this knowledge gap, this paper presents a design space for a custom environmental sensing and management system designed and developed to fill the data gaps in low-resource urban settings with a particular focus on African cities. The paper presents the AirQo system, which is the first instance of the design space requirements. The AirQo system includes: (1) autonomous AirQo sensors designed and customised to be deployed in resource constrained environments (2) a distributed sensor network that includes over 120 static and mobile nodes for air quality sensing (3) AirQo network manager tool for tracking and management of installation and maintenance of nodes, (4) AirQo platform that provides calibration, data access and analytics tools to support usage among policy makers and citizens. Case studies from African cities that are using the data and insights for education, awareness and policy are presented. The paper provides a template for designing and deploying a technology-driven solution for cities in low resource settings. [ABSTRACT FROM AUTHOR]

This paper investigates the effect of the architectural design of deep learning models in combination with a feature engineering approach considering the temporal variation in the features in the case of tropospheric ozone forecasting. Although deep neural network models have shown successful results by extracting features automatically from raw data, their performance in the domain of air quality forecasting is influenced by different feature analysis approaches and model architectures. This paper proposes a simple but effective analysis of tropospheric ozone time series data that can reveal temporal phases of the ozone evolution process and assist neural network models to reflect these temporal variations. We demonstrate that addressing the ozone evolution phases when developing the model architecture improves the performance of deep neural network models. As a result, we evaluated our approach on the CNN model and showed that not only does it improve the performance of the CNN model, but also that the CNN model in combination with our approach boosts the performance of the other deep neural network models such as LSTM. The development of the CNN, LSTM-CNN, and CNN-LSTM models using the proposed approach improved the prediction performance of the models by 3.58%, 1.68%, and 3.37%, respectively. [ABSTRACT FROM AUTHOR]

Air pollution is an important factor affecting human health and daily life. The Chinese government is making vigorous efforts to control air pollution. The upgrading of the industrial structure is a problem-solving tool in the environment and economic growth cases. This paper aims to explore the relationships among environmental regulation, the upgrading of the industrial structure and air pollution. The PVAR (Panel Vector Auto Regression) model and moderating effect model are used to conduct empirical analysis based on panel data of 30 provinces in China from 2004 to 2020. The analysis of the results provides the following findings. Firstly, environmental regulations can significantly reduce emissions, but the deterioration of air quality does not have a significant impact on the improvement of environmental regulations. Secondly, industrial structure upgrading can reduce air pollution, but the worsening of the air quality will hinder the upgrading of industrial structures. Thirdly, environmental regulation can promote industrial structure upgrading. Lastly, industrial structure upgrading is a moderating variable and can positively moderate the impact of environmental regulations on air pollution. [ABSTRACT FROM AUTHOR]

In Canada, Indigenous populations are disproportionately threatened by wildfire smoke and the associated adverse health impacts. This paper presents the results of a narrative review of 51 academic and related resources which explored protective action decision making during wildfire smoke events within Indigenous communities in Canada. A search of scholarly articles and other relevant sources yielded resources which were subject to thematic analysis and described in order to present a narrative review of current knowledge and gaps in research. A small and growing literature provides insights into protective actions taken by the general population during wildfire smoke events, but very little is known about protective actions taken by Indigenous peoples in Canada during wildfire smoke events. This lack of understanding hinders the capacity of decision makers to improve emergency management and minimize community health impacts of wildfire smoke. [ABSTRACT FROM AUTHOR]

In the last few decades, urban planning has expanded regarding environmental considerations. However, air quality, which is regarded as an important aspect of the green development of cities, is not considered in urban planning. This research aims to propose a tool to easily introduce air quality considerations into urban projects. Nowadays, the usual air pollutants (NOx, PM, SO2, and O3) are measured via sophisticated monitoring stations—or even low-cost devices—to give near-real-time air quality indices. However, stations are not adapted to local air pollution and real-time data are not helpful for planning purposes. An index able to rank areas and projects based on urban "air proxy data" would help decision makers. This paper presents how to create an air quality index as a decision support tool for urban planning. No pollutant measurement campaign will be necessary and only data that are easily accessible, even to nonexperts, are used. This paper describes the methodological development of an index that we call AQOI (Air Quality Observed Index), and the results obtained for four different locations (industrial, urban, and rural) considered as preliminary tests. [ABSTRACT FROM AUTHOR]

This report provides a review of several measurement studies and campaigns focusing on road dust resuspension (RDS), which is becoming an important source of ambient particulate matter (PM) concentrations. There is a growing requirement from stakeholders, policy makers and scientists for gathering additional data around RDS, since there is a lack of well-established methodologies or those developed for measuring or modelling this source of emissions are not very recent. Moreover, conventional inventory methodologies are not available for this source of emissions, and it is not yet covered in the UK's National Atmospheric Emissions Inventory (NAEI) neither in the inventory guidebooks at European level. Based on the available literature and models, this paper also seeks to provide some preliminary considerations whether an approach can be tailored for application in air quality models used for modelling ambient PM concentrations in the UK in a consistent manner and demonstrated using a state-of-art air quality modelling tool, namely RapidAir®. The impact of including resuspension emissions on roadside concentrations was modelled in a UK case study with the greatest increases in modelled road PM10 concentrations observed on or near the road surface. The paper concludes with recommendations for longer-term implementation of the proposed improvements such as investigation of the temporal effects of RDS on PM levels; of the influence of different type of tyre materials and mixture (e.g., composition) and designs (e.g., shape, dimensions and tread); and influence of different existing pavements or of new road surface types. [ABSTRACT FROM AUTHOR]

High P M 10 concentrations are still a significant problem in many parts of the world. In many countries, including Poland, 50 μ g/m 3 is the permissible threshold for a daily average P M 10 concentration. The number of people affected by this threshold's exceedance is challenging to estimate and requires high-resolution concentration maps. This paper presents an application of random forests for downscaling regional model air quality results. As policymakers and other end users are eager to receive detailed-resolution P M 10 concentration maps, we propose a technique that utilizes the results of a regional CTM (GEM-AQ, with 2.5 km resolution) and a local Gaussian plume model. As a result, we receive a detailed, 250 m resolution P M 10 distribution, which represents the complex emission pattern in a foothill area in southern Poland. The random forest results are highly consistent with the GEM-AQ and observed concentrations. We also discuss different strategies of training random forest on data using additional features and selecting target variables. [ABSTRACT FROM AUTHOR]

Air quality monitoring in polluted environments is of great significance to human health. Traditional methods use various pieces of meteorological equipment, which have limited applications in complex terrains and high costs. In this paper, a novel idea is put forward to solve the problem of air pollution monitoring in urban areas. We investigate whether air quality can be assessed visually by examining the haziness of photos from a far distance. Specifically, the correlation between the air quality indexes, such as the AQI, PM2.5, and PM10, of real outdoor scenarios and the haziness level evaluation scores of the monitoring images is calculated. The results show that the objective indicators can indeed reflect the level of air pollution, and the degree of correlation is invariant to the image size. To apply this new observation to a practical system, a novel method called fastDBCP (fast dark and bright channel prior) is developed. Based on a down-sampling strategy, a ratio is calculated between the dark and bright channel prior information in scaled images and adopted as the visual index of air pollution. Our experimental results show that the proposed metric not only demonstrates advantages in terms of its correlation degree and computational speed, but also shows a high level of classification accuracy compared to that of competing metrics. [ABSTRACT FROM AUTHOR]

A natural experiment conducted on the shrinking Salton Sea, a saline lake in California, showed that each one foot drop in lake elevation resulted in a 2.6% average increase in P M 2.5 concentrations. The shrinking has caused the asthma rate continues to increase among children, with one in five children being sent to the emergency department, which is related to asthma. In this paper, several data-driven machine learning (ML) models are developed for forecasting air quality and dust emission to study, evaluate and predict the impacts on human health due to the shrinkage of the sea, such as the Salton Sea. The paper presents an improved long short-term memory (LSTM) model to predict the hourly air quality ( O 3 and CO ) based on air pollutants and weather data in the previous 5 h. According to our experiment results, the model generates a very good R2 score of 0.924 and 0.835 for O 3 and CO , respectively. In addition, the paper proposes an ensemble model based on random forest (RF) and gradient boosting (GBoost) algorithms for forecasting hourly P M 2.5 and P M 10 using the air quality and weather data in the previous 5 h. Furthermore, the paper shares our research results for P M 2.5 and P M 10 prediction based on the proposed ensemble ML models using satellite remote sensing data. Daily P M 2.5 and P M 10 concentration maps in 2018 are created to display the regional air pollution density and severity. Finally, the paper reports Artificial Intelligence (AI) based research findings of measuring air pollution impact on asthma prevalence rate of local residents in the Salton Sea region. A stacked ensemble model based on support vector regression (SVR), elastic net regression (ENR), RF and GBoost is developed for asthma prediction with a good R2 score of 0.978. [ABSTRACT FROM AUTHOR]

The RHAPS (Redox-Activity And Health-Effects Of Atmospheric Primary And Secondary Aerosol) project was launched in 2019 with the major objective of identifying specific properties of the fine atmospheric aerosol from combustion sources that are responsible for toxicological effects and can be used as new metrics for health-related outdoor pollution studies. In this paper, we present the overall methodology of RHAPS and introduce the phenomenology and the first data observed. A comprehensive physico-chemical aerosol characterization has been achieved by means of high-time resolution measurements (e.g., number size distributions, refractory chemical components, elemental composition) and low-time resolution analyses (e.g., oxidative potential, toxicological assays, chemical composition). Preliminary results indicate that, at the real atmospheric conditions observed (i.e., daily PM1 from less than 4 to more than 50 μg m−3), high/low mass concentrations of PM1, as well as black carbon (BC) and water soluble Oxidative Potential (WSOP,) do not necessarily translate into high/low toxicity. Notably, these findings were observed during a variety of atmospheric conditions and aerosol properties and with different toxicological assessments. Findings suggest a higher complexity in the relations observed between atmospheric aerosol and toxicological endpoints that go beyond the currently used PM1 metrics. Finally, we provide an outlook to companion papers where data will be analyzed in more detail, with the focus on source apportionment of PM1 and the role of source emissions on aerosol toxicity, the OP as a predictive variable for PM1 toxicity, and the related role of SOA possessing redox-active capacity, exposure-response relationships for PM1, and air quality models to forecast PM1 toxicity. [ABSTRACT FROM AUTHOR]

Integrated assessment modelling (IAM) has been successfully used in the development of international agreements to reduce transboundary pollution in Europe, based on the GAINS model of IIASA. At a national level in the UK, a similar approach has been taken with the UK Integrated Assessment Model, UKIAM, superimposing pollution abatement measures and behavioural change on energy projections designed to meet targets set for the reduction of greenhouse gas emissions and allowing for natural and imported contributions from other countries and shipping. This paper describes how the UKIAM was used in the development of proposed targets for the reduction of fine particulate PM2.5 in the UK Environment Act, exploring scenarios encompassing different levels of ambition in reducing the emissions of air pollutants up to 2050, with associated health and other environmental benefits. There are two PM2.5 targets, an annual mean concentration target setting a maximum concentration to be reached by a future year, and a population exposure reduction target with benefits for health across the whole population. The work goes further, also demonstrating links to social deprivation. There is a strong connection between climate measures aimed at reducing net GHG emissions to zero by 2050 and future air quality, which may be positive or negative, as illustrated by sectoral studies for road transport where electrification of the fleet needs to match the evolution of energy production, and for domestic heating, where the use of wood for heating is an air quality issue. The UKIAM has been validated against air pollution measurements and other types of modelling, but there are many uncertainties, including future energy projections. [ABSTRACT FROM AUTHOR]

This paper explored the changes of six significant pollutants (PM2.5, PM10, SO2, NO2, O3, and CO) in Jilin City during the coronavirus disease 2019 (COVID-19) epidemic in 2022, and compared them with the same period of previous years to analyze the impact of anthropogenic emissions on the concentration of pollutants; The Weather Research and Forecasting Community Multiscale Air Quality (WRF–CMAQ) model was used to evaluate the effect of meteorological factors on pollutant concentration. The results showed that except for O3, the concentrations of the other five pollutants decreased significantly, with a range of 21–47%, during the lockdown period caused by the government's shutdown and travel restrictions. Compared with the same period in 2021, the decrease of PM2.5 was only 25% of PM10. That was because there was still a large amount of PM2.5 produced by coal-fired heating during the blockade period, which made the decrease of PM2.5 more minor. A heavy pollution event caused by adverse meteorological conditions was found during the lockdown period, indicating that only controlling artificial emissions cannot eliminate the occurrence of severe pollution events. The WRF–CMAQ results showed that the lower pollutant concentration in 2022 was not only caused by the reduction of anthropogenic emissions but also related to the influence of favorable meteorological factors (higher planetary boundary layer thickness, higher wind speed, and higher temperature). [ABSTRACT FROM AUTHOR]

Air quality data sets are widely used in numerous analyses. Missing values are ubiquitous in air quality data sets as the data are collected through sensors. Recovery of missing data is a challenging task in the data preprocessing stage. This task becomes more challenging in time series data as time is an implicit variable that cannot be ignored. Even though existing methods to deal with missing data in time series perform well in situations where the percentage of missing values is relatively low and the gap size is small, their performances are reasonably lower when it comes to large gaps. This paper presents a novel algorithm based on seasonal decomposition and elastic net regression to impute large gaps of time series data when there exist correlated variables. This method outperforms several other existing univariate approaches namely Kalman smoothing on ARIMA models, Kalman smoothing on structural time series models, linear interpolation, and mean imputation in imputing large gaps. However, this is applicable only when there exists one or more correlated variables with the time series with large gaps. [ABSTRACT FROM AUTHOR]

Background: Air pollution is an important factor affecting residents' health. Methods: Based on the health data of 9959 residents from 28 provinces in China in the 2017 Chinese General Social Survey (CGSS), this paper uses a hierarchical linear model (HLM) to scientifically analyze the impact of air pollution on residents' health and to explore the moderating role of air pollution in various factors influencing residents' health. Results: Air pollution has significant negative effects on residents' health (coef. −0.103, p < 0.01), while frequency of exercise (coef. 0.070, p < 0.01), education level (coef. 0.012, p < 0.1) and income level (coef. 0.383, p < 0.01) have positive effects on residents' health. Air pollution has a negative moderating effect on the positive impact of exercise frequency on residents' health (coef. −0.015, p = 0.004). Conclusions: Air pollution causes serious damage to residents' health and it is necessary to control air pollution. Increasing residents' income, education level and exercise frequency can help improve residents' health level. Air pollution significantly reduce the positive impact of exercise frequency on residents' health. In order to effectively improve their own health, it is a better choice for residents to exercise when the air quality is good. [ABSTRACT FROM AUTHOR]

The Euro 7 legislation and the Zero-Impact Emissions concept aim at significantly improving air quality. Technologies that reduce pollutant emissions beyond current gasoline passenger cars have already been intensively investigated, but a holistic system layout considering extended boundary conditions is missing so far. This paper therefore develops technical solutions to achieve a Euro 7 scenario and Zero-Impact Emissions for a 2030+ vehicle. First, challenging test scenarios are identified to develop compliant vehicles. The scenarios cover extreme conditions in real-world driving, such as hot and cold ambient conditions, stop-and-go in rural areas or high speed and steep gradients on highways. Different technology options are discussed and selected for the investigations. An empirical–physical simulation model for the exhaust gas aftertreatment system is extended with new technologies, such as an electrical heater disc in front of the catalyst or a burner in the exhaust system. In addition to stoichiometric engine operation and increased catalyst volume, the results show that the expected Euro 7 regulations can be achieved in all extreme scenarios by combining additional exhaust gas heating with engine power limitation or pre-heating. Moreover, even Zero-Impact Emissions are achieved in most cases with the same technology options. [ABSTRACT FROM AUTHOR]

In China, blue sky defense is a crucial part of ecological environment governance. Objective environmental governance performance needs to be perceived by the public to more truly affect the public's evaluation of the government's environmental governance. This paper focuses on the public's subjective perception of air pollution and evaluation of the local government's environmental governance. Based on the Chinese General Social Survey data, the matched economic indicators, and air pollution data, we conduct a diachronic study on the public's evaluation of local governments' environmental protection work, and we analyze the relationship between the subjective perception of air pollution, the objective air pollution data, and the evaluation of local governments' environmental protection work. The results showed the following: (1) People's evaluation of local governments' environmental protection work significantly improved from 2013 to 2021. The objective indicator improved, while the subjective perception declined. (2) The subjective perception of air pollution has a significant negative impact on the evaluation of local governments' environmental protection work and needs to be better considered to improve air quality. At the same time, the effect of the objective indicator is insignificant. [ABSTRACT FROM AUTHOR]

Air pollution is one of the most important topics as it can cause various reactions of the organisms, such as mental health disorders, respiratory problems or various cardiovascular despises. Many of the side effects of pollution are caused by particulate matter (PM). Therefore air pollution, especially the concentration of PM is monitored in many European countries. In the past years, Skopje has been one of the top-ranked cities in the world concerning the concentration of PM. This paper investigates the effect of the pandemic with COVID-19 and the restriction measures on air quality. The data collected before the pandemic (May 2018), during the global pandemic (May 2020 and May 2021), and after the period with restriction measures (May 2022) are analyzed. The measurement parameters are collected at the technical campus of the Ss Cyril and Methodius University in Skopje, North Macedonia, in May 2018, May 2020, May 2021, and May 2022. In this research, it can be confirmed that the restriction measures had a significant positive impact on air pollution. [ABSTRACT FROM AUTHOR]

A large petrochemical complex, dubbed Petro–complex, situated in a rather rural region of Taiwan, was used as a test bed to detect emissions from the Petro–complex to its surroundings. Hourly observations of speciated non–methane hydrocarbons (NMHCs) by the photochemical assessment monitoring stations (PAMSs), as well as the total amounts of NMHCs, SO2, and NOx provided by the air quality stations (AQSs), were utilized to find useful petro–emission indication methods. The analytical aspect of NMHCs either as a speciated form or as total amounts was demonstrated through field comparison to illustrate data quality. Using ethyne to offset traffic influence, the ratios of ethene to ethyne (acetylene) (E/A) and propene to ethyne (P/A) were proven to be effective indicators of petro–emissions owing to pronounced emissions of ethene and propene, revealed as tall spikes in PAMS measurements. SO2 and NOx were also explored as petro–emission indicators mainly for stack sources. By coordinating with three–dimensional modeling, SO2 from petro–emissions could be distinguished from other prominent sources, such as coal–fired power plants. An attempt was also made to use SO2 and NOx as indicators of broader petro–emissions with stringent criteria to minimize traffic interference and increase specificity. Similar findings were observed with the three indicators, that is, volatile organic compounds (VOCs) ratios, SO2 and NOx, to identify the southwest area of the Petro–region as the most affected area, as represented by Taisi station (F2). The percent affected time of a year at F2 was 10%–14%, owing to the dominant wind field of northeast monsoonal (NEM) in the region, as compared with other sites in the east and north of 1–5%. Using VOC ratios as petro–emission indicators is more effective than using other gases, owing to the compositional advantage to minimize traffic interference. [ABSTRACT FROM AUTHOR]

To monitor the spread of the novel coronavirus (COVID-19), India, during the last week of March 2020, imposed national restrictions on the movement of its citizens (lockdown). Although India's economy was shut down due to restrictions, the nation observed a sharp decline in particulate matter (PM) concentrations. In recent years, Delhi has experienced rapid economic growth, leading to pollution, especially in urban and industrial areas. In this paper, we explored the linkages between air quality and the nationwide lockdown of the city of Delhi using a geographic information system (GIS)-based approach. Data from 37 stations were monitored from 12 March, 2020 to 2 April, 2020 and it was found that the Air Quality Index for the city was almost reduced by 37% and 46% concerning PM2.5 and PM10, respectively. The study highlights that, in regular conditions, the atmosphere's natural healing rate against anthropogenic activities is lower, as indicated by a higher AQI. However, during the lockdown, this sudden cessation of anthropogenic activities leads to a period in which the natural healing rate is greater than the induced disturbances, resulting in a lower AQI, and thus proving that this pandemic has given a small window for the environment to breathe and helped the districts of Delhi to recover from serious issues related to bad air quality. If such healing windows are incorporated into policy and decision-making, these can prove to be effective measures for controlling air pollution in heavily polluted regions of the World. [ABSTRACT FROM AUTHOR]

High urbanization and a consequent change in land cover can lead to a deterioration in air quality and generate impacts on public health. The objective of this paper is to provide a multitemporal analysis of the influence of particulate matter ≤ 10 μm (PM10) on human mortality from the land cover variation in a Latin American megacity. Six monitoring stations (monitoring daily PM10 concentration, increases in daily mortality (IDM), and land cover) were established throughout the megacity. The results suggest that for every 10% increase in vegetation cover, the daily PM10 concentration and IDM decreases by 7.5 μg/m3 and 0.34%, respectively. Moreover, it is evident that the monitoring station with the lowest vegetation cover (8.96 times) shows an increase of 1.56 times and 4.8 times in the daily PM10 concentration and IDM, respectively, compared with the monitoring station with the highest vegetation cover (46.7%). It is also suggested that for each increase of 100 inhabitants/hectare in population density, the daily PM10 concentration and IDM increases by 9.99 µg/m3 and 0.45%, respectively. Finally, the population densification of the megacity possibly implies a loss of vegetation cover and contributes to the increase in PM10 and IDM. [ABSTRACT FROM AUTHOR]

In recent years, the indoor air quality (IAQ) of educational buildings has attracted people's attention. As a resting place, the dormitory occupies more than half of the students' time in school. During sleep, the IAQ in dormitories is easily affected by breathing, which in turn affects the sleep quality and mental state of students. In order to study the relevant IAQ during sleep, this paper selected the dormitories of graduate students of different grades in a university in northern China, and monitored the temperature, humidity, CO2, PM2.5, HCHO and TVOC for two weeks during the changing seasons of autumn and winter. In addition, by issuing questionnaires, students made a subjective evaluation of the IAQ. According to the results of objective monitoring data and subjective evaluation, the IAQ changes in student dormitories of different grades, genders, and locations are statistically analyzed, and the correlation between environmental parameters is discussed. The research results show that temperature and humidity basically meet the national standards; indoor PM2.5 is positively correlated with outdoor PM2.5; and HCHO and TVOC are positively correlated with indoor temperature and humidity. Most dormitories lack natural ventilation, and the concentration of CO2 during sleep is too high, which affects the quality of sleep and mental state. [ABSTRACT FROM AUTHOR]

Prior studies conducted within the realm of environmental psychology and public health have shown that air pollution exposure exerts significant effects on both physical and psychological well-being, especially for children. The roles of air pollution exposure are being increasingly recognized as essential factors influencing children's restoration. This systematic review provides an overview of existing knowledge of the impact of air pollution on children's mental health and restorative experience in both outdoor and indoor environments. A list of keywords for paper selection was derived from a systematic investigation of the literature on children's restorative environments. A total of 228 studies were initially identified, 18 of which met the eligibility criteria. This systematic review summarizes distinctive categories of air pollutants and discusses the assessments for both air pollution exposure and children's restorative outcomes. Additionally, 16 barriers in air pollution exposure to children's restorative experience were identified. The findings of this systematic review were concluded in an integrated framework, which have important implications for developing evidence-based and cross-disciplinary research on the air quality and children's restoration. [ABSTRACT FROM AUTHOR]

In this study, we investigate the air quality improvement effect in Busan, the largest port city in South Korea, caused by the implementation of International Maritime Organization (IMO) sulfur regulations. Currently, the Korean government is struggling with problems related to PM2.5, and ships are one of the major sources of PM2.5 generation in South Korea. Therefore, we tried to estimate how much the PM2.5 levels in South Korea could be improved via low-sulfur regulation. According to the Clean Air Quality Policy Support System (CAPSS; National Emission Inventory) in 2016, ship emissions in Busan accounted for 39%, 71%, and 39% of PM2.5, SO2, and NO2 emissions, respectively. To simulate the effect of the IMO's 0.5 percent sulfur regulation, SOx and PM2.5 emissions from oil-fueled cargo ships were reduced. Via ship fuel regulation, the PM2.5 concentration was improved by up to 19% at a site near the port in 2020. In addition, in the case of sulfate, the reduction rate was higher on the downwind side of the Busan port and not near the port, which can be considered as the cause of advection and secondary formation. The PM2.5 contributions from ships to each of the sub-regions in Busan also decreased by an average of 47% because of IMO sulfur regulation. Although there were limitations in terms of emission estimations because of the application of low-sulfur regulation, we expect that the results of this paper can be used for additional PM2.5 improvement plans developed by the Korean government and by the local government as well. [ABSTRACT FROM AUTHOR]

Air pollution is a global problem, which needs to be understood and controlled to ensure a healthy environment and inform sustainable development. Urban areas have been established as one of the main contributors to air pollution, and, as such, urban air quality is the subject of an increasing volume of research. One of the principal means of studying air pollution dispersion is to use computational fluid dynamics (CFD) models. Subject to careful verification and validation, these models allow for analysts to predict air flow and pollution concentration for various urban morphologies under different environmental conditions. This article presents a detailed review of the use of CFD to model air pollution dispersion in an urban environment over the last decade. The review extracts and summarises information from nearly 90 pieces of published research, categorising it according to over 190 modelling features, which are thematically systemised into 7 groups. The findings from across the field are critically compared to available urban air pollution modelling guidelines and standards. Among the various quantitative trends and statistics from the review, two key findings stand out. The first is that, despite the existence of best practice guidelines for pollution dispersion modelling, anywhere between 12% and 34% of the papers do not specify one or more aspects of the utilised models, which are required to reproduce the study. The second is that none of the articles perform verification and validation according to accepted standards. The results of this review can, therefore, be used by practitioners in the field of pollution dispersion modelling to understand the general trends in current research and to identify open problems to be addressed in the future. [ABSTRACT FROM AUTHOR]

Very low air quality in the Warsaw conurbation, Poland, similarly to the case in many large European cities, poses a serious threat to the residents' health, being a significant source of premature mortality. Many results presented in earlier publications indicated local heating installations and car traffic as the main emission categories responsible for this adverse population exposure, where the dominant polluting compounds are NOx, PM10, PM2.5, and BaP. The last two mainly originate from individual household heating installations, both in the city of Warsaw and in its vicinity. To reduce the health risk of air pollution, the city authorities have recently made fundamental decisions, related to the individual housing sector, aimed at the radical decarbonization of all heating installations in Warsaw and its surroundings. On the other hand, the ongoing modernization of the city's car fleet (including individual and public transport), taking into account the restrictive EU emission standards, as well as the quickly growing share of electric and hybrid cars (BEVs and PHEVs), gives a good prospect of a fundamental improvement in air quality in Warsaw conurbation. The main subject of the paper is a quantitative assessment of the air quality improvement in the current decade (by 2030), resulting from the above modernization activities. The final results are expressed as the attributed reduction in population exposure, which was found to be 28–30% with respect to NOx and PM, and the associated health risk, i.e., 204 fewer avoidable deaths with respect to NOx and 607 fewer with respect to PM2.5. [ABSTRACT FROM AUTHOR]

In-vehicle air quality monitoring systems have been seen as promising paradigms for monitoring drivers' conditions while they are driving. This is because some in-vehicle cabins contain pollutants that can cause drowsiness and fatigue to drivers. However, designing an efficient system that can predict in-vehicle air quality has challenges, due to the continuous variation in parameters in cabin environments. This paper presents a new approach, using deep learning techniques that can deal with the varying parameters inside the vehicle environment. In this case, two deep learning models, namely Long-short Term Memory (LSTM) and Gated Recurrent Unit (GRU) are applied to classify and predict the air quality using time-series data collected from the built-in sensor hardware. Both are compared with conventional methods of machine learning models, including Support Vector Regression (SVR) and Multi-layer Perceptron (MLP). The results show that GRU has an excellent prediction performance with the highest coefficient of determination value (R2) of 0.97. [ABSTRACT FROM AUTHOR]

Accurate prediction of PM2.5 concentration is one of the key tasks of air pollution assessment, early warning, and treatment. In this paper, four monitoring sites were arranged in Jiangbei New District of Nanjing City, China. The environmental parameters such as PM2.5/PM10 concentration, temperature, and humidity were monitored from January to February 2020. A gated recurrent unit (GRU) network based on the PM2.5 concentration prediction model was established to predict PM2.5 concentration. The mean relative error (MRE), root mean square error (RMSE), and Pearson correlation coefficient were selected as the evaluation criteria for the accuracy of the GRU model. The data set was divided into a training set, a test set and a validation set at a ratio of 7:2:1, and the GRU model was used to predict the hourly value of PM2.5 concentration in the next week. The prediction results show that the Pearson correlation coefficients between the predicted values and the monitored values of the four monitoring sites have reached more than 0.9, reflecting a strong correlation. The relative average errors are around 10%. The GRU model prediction of NJAU (Nanjing Agricultural University)-Pukou Campus Site is the most accurate, and the correlation coefficient, MRE, and RMSE are 0.970, 7.85%, and 9.6049, respectively, reflecting the good prediction performance of the model. Therefore, this research supports the prediction of air quality in different cities and regions, so people can take protective measures in advance and reduce the damage caused by air pollution to human bodies. [ABSTRACT FROM AUTHOR]

A precise air pollution forecast is the basis for targeted pollution control and sustained improvements in air quality. It is desirable and crucial to select the most suitable model for air pollution forecasting (APF). To achieve this goal, this paper provides a comprehensive evaluation of performances of different models in simulating the most common air pollutants (e.g., PM2.5, NO2, SO2, and CO) in Guangzhou (23.13° N, 113.26° E), China. To simulate temporal variations of the above-mentioned air pollutant concentrations in Guangzhou in September and October 2020, we use a numerical forecasting model (i.e., the Weather Research and Forecasting model with Chemistry (WRF-Chem)) and two artificial intelligence models (i.e., the back propagation neural network (BPNN) model and the long short-term memory (LSTM) model). WRF-Chem is also used to simulate the meteorological elements (e.g., the 2 m temperature (T2), 2 m relative humidity (RH), and 10 m wind speed and direction (WS, WD)). In order to investigate the simulation accuracies of classical APF models, we simultaneously compare the simulations of the WRF-Chem, BPNN, and LSTM models to ground truth observations. Comparative assessment results show that WRF-Chem simulated air pollutant (i.e., PM2.5, NO2, SO2, and CO) concentrations have the best correlations with ground measurements (i.e., Pearson correlation coefficient R = 0.88, 0.73, 0.61, and 0.61, respectively). Furthermore, to evaluate model performance in terms of accuracy and stability, the normalized mean bias (NMB, %) and mean fractional bias (MFB, %) are adopted as the standard performance metrics (SPMs) proposed by Boylan et al. The comparison results indicate that when simulating PM2.5, WRF-Chem was more effective than the BPNN but less effective than the LSTM. While simulating concentrations of NO2, SO2, and CO, the WRF-Chem model performed better than the BPNN and LSTM models. With regards to WRF-Chem, the NMBs and MFBs for the PM2.5 simulations are, respectively, 6.49% and 0.02%, –11.96% and –0.031% for NO2, 7.93% and 0.019% for CO, and 5.04% and 0.012% for SO2. Our results suggest that WRF-Chem has superior performance and better accuracy than the NN-based prediction models, making it a promising and useful tool to accurately predict and forecast regional air pollutant concentrations on a city scale. [ABSTRACT FROM AUTHOR]

Exposure to air pollutants, such as PM2.5 and ozone, has a serious adverse effect on health, with more than 4 million deaths, including early deaths. Air pollution in ports is caused by exhaust gases from various elements, including ships, and to reduce this, the International Maritime Organization (IMO) is also making efforts to reduce air pollution by regulating the sulfur content of fuel used by ships. Nevertheless, there is a lack of measures to identify and minimize the effects of air pollution. The Community Multiscale Air Quality (CMAQ) model is the most used to understand the effects of air pollution. In this paper, we propose a hybrid model combining the CMAQ model and RNN-LSTM, an artificial neural network model. Since the RNN-LSTM model has very good predictive performance, combining these two models can improve the spatial distribution prediction performance of a large area at a relatively low cost. In fact, as a result of prediction using the hybrid model, it was found that IOA improved by 0.235~0.317 and RMSE decreased by 4.82~8.50 μg/m3 compared to the case of using only CMAQ. This means that when PM2.5 is predicted using the hybrid model, the accuracy of the spatial distribution of PM2.5 can be improved. In the future, if real-time prediction is performed using the hybrid model, the accuracy of the calculation of exposure to air pollutants can be increased, which can help evaluate the impact on health. Ultimately, it is expected to help reduce the damage caused by air pollution through accurate predictions of air pollution. [ABSTRACT FROM AUTHOR]

The problem of air pollution has attracted more and more attention. PM2.5 is a key factor affecting air quality. In order to improve the prediction accuracy of PM2.5 concentration and make people effectively control the generation and propagation of atmospheric pollutants, in this paper, a long short-term memory neural network (LSTM) model based on principal component analysis (PCA) and attention mechanism (attention) is constructed, which first uses PCA to reduce the dimension of data, eliminate the correlation effect between indicators, and reduce model complexity, and then uses the extracted principal components to establish a PCA-attention-LSTM model. Simulation experiments were conducted on the air pollutant data, meteorological element data, and working day data of five cities in Ningxia from 2018 to 2020 to predict the PM2.5 concentration. The PCA-attention-LSTM model is compared with the support vector regression model (SVR), AdaBoost model, random forest model (RF), BP neural network model (BPNN), and long short-term memory neural network (LSTM). The results show that the PCA-attention-LSTM model is optimal; the correlation coefficients of the PCA-attention-LSTM model in Wuzhong, Yinchuan, Zhongwei, Shizuishan, and Guyuan are 0.91, 0.93, 0.91, 0.91, and 0.90, respectively, and the SVR model is the worst. The addition of variables such as a week, precipitation, and temperature can better predict PM2.5 concentration. The concentration of PM2.5 was significantly correlated with the geographical location of the municipal area, and the overall air quality of the southern mountainous area was better than that in the northern Yellow River irrigation area. PM2.5 concentration shows a clear seasonal change trend, with the lowest in summer and the highest in winter, which is closely related to the climate environment of Ningxia. [ABSTRACT FROM AUTHOR]

In China, traditional food is a significant element of culture that affects human behaviors. The point of interest (POI) of traditional food restaurants' location and their volatile organic compounds (VOCs) emissions affect the urban ecology. Rather than examine potential urban development patch (PUDP) based on land use data, the perspective of this paper is to examine the PUDP, air quality, and food culture in urban ecology in Chengdu, China. Methods: First, the research identifies three types of PUDP models (open PUDP, landscape PUDP, and conflict PUDP) with the weighted overlay of land use data, then uses machine learning to examine the relationship between PUDP, POI of traditional food restaurant, and VOCs. Results: The study generates three types of PUDP which are open PUDP, landscape PUDP, and conflict PUDP. VOCs and POI of traditional restaurant have a strong correlation, and both have a significant negative correlation with open PUDP. However, the landscape PUDP and conflict PUDP do not show an obvious relationship with food POI and VOCs. Conclusion: The results indicate that the future urban ecology should consider restaurant location, VOCs from restaurants, and their relationship to urban land use data as they have a strong relationship. [ABSTRACT FROM AUTHOR]

Low-carbon cities have become a new trend in regional development around the world. Whether they can improve the environment in China, especially the air quality, remains to be tested. In this paper we take low-carbon city construction as a quasi-natural experiment and empirically test the net effects, influencing factors, and dynamic effects of low-carbon city construction on air quality by constructing a multistage propensity score matching and Difference-in-Differences model. After a series of robustness tests, the following conclusions are drawn: first, low-carbon city construction reduces the regional Air Quality Index, inhalable particulate matter, fine particulate matter, and NO2 concentrations. Among them, the construction effect in 2017 was the most significant. Therefore, it is necessary to continue to promote low-carbon city policies and accurately identify different types of air pollutants to improve the overall effectiveness of low-carbon city policies. Second, temperature, humidity, wind level, and other meteorological factors, as well as gross domestic product for the proportion of secondary industry, will affect air quality. Therefore, it is necessary to comprehensively consider meteorological, economic, social, and other influencing factors in an early stage of the construction of the next batch of low-carbon cities, so as to avoid falling into the trap of "building first and managing later". Third, the impact of secondary industry on air quality is significantly greater than that of tertiary industry. Therefore, the upgrading of industrial structure promoted by low-carbon city policy is effective in improving air quality. Fourth, the construction of low-carbon cities in western China has the most significant impact on air quality improvement. Therefore, the joint prevention and control mechanism of air pollution control in urban agglomeration should be established. [ABSTRACT FROM AUTHOR]

This paper collects and analyzes the 1954–2017 monthly average reanalysis data from the U.S. National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR), the 1954–2017 haze days observation data from the National Meteorological Information Center/China Meteorological Administration (NMIC/CMA) and the PM2.5 daily average mass concentration data in 2013–2017 from the China Air Quality Online Monitoring Platform. The atmospheric apparent heat source Q1 (negative Q1 means cold source in winter) over the Tibetan Plateau in December of 1954–2017 is estimated based on thermodynamic equations, and the East Asian winter monsoon index (EAWMI) is calculated. In addition, the discrepancies of the air quality among China's five typical regions (Beijing–Tianjin–Hebei, Fen-Wei Plain, Yangtze River Delta, Pearl River Delta and Sichuan-Chongqing regions) under the joint influence of the Q1 and EAWMI are studied. Results show that when it is a strong cold source year, abnormal downdrafts and "temperature inversion covers" occur in areas far from the high terrain, resulting in increased pollution, while the opposite is true in weak cold source years. In strong EAWMI years, there is an abnormal northerly sinking cold flow in the lower layers of mid-high latitudes, which increases the pollution in the area south of 30° N, and the opposite is true in weak EAWMI years. Affected by the combined activities of the Q1 and the EAWMI, the meteorological conditions of the five typical regions are different, and thus present different air pollution characteristics. [ABSTRACT FROM AUTHOR]

Missing values in air quality datasets bring trouble to exploration and decision making about the environment. Few imputation methods aim at time series air quality data so that they fail to handle the timeliness of the data. Moreover, most imputation methods prefer low-missing-rate datasets to relatively high-missing-rate datasets. This paper proposes a novel missing data imputation method, called FTLRI, for time series air quality data based on the traditional logistic regression and a presented "first Five & last Three" model, which can explain relationships between disparate attributes and extract data that are extremely relevant, both in terms of time and attributes, to the missing data, respectively. To investigate the performance of FTLRI, it is benchmarked with five classical baselines and a new dynamic imputation method using a neural network with average hourly concentration data of pollutants from three disparate stations in Lanzhou in 2019 under different missing rates. The results show that FTLRI has a significant advantage over the compared imputation approaches, both in the particular short-term and long-term time series air quality data. Furthermore, FTLRI has good performance on datasets with a relatively high missing rate, since it only selects the data extremely related to the missing values instead of relying on all the other data like other methods. [ABSTRACT FROM AUTHOR]

The ventilation performance of air shafts is important to the air quality of subway tunnels, but there is no unified evaluation index of ventilation performance. In this paper, the air shafts at different locations in subway tunnels were taken as research objects, and the wind speed as well as the particulate matter concentration of each air shaft was tested. The effective ventilation volume and PM2.5 discharge efficiency of the air shafts were defined to evaluate the ventilation performance. It was found that on average, during the subway train service, the station air shaft on the train-arriving side can discharge 2050 m3 of dirty air in the tunnels and inhale 218 m3 of fresh air from the outside environment, while the station air shaft on the train-leaving side can absorb 2430 m3 of fresh air but can hardly effectively discharge dirty air; meanwhile, the middle air shaft can not only effectively exhaust 1519 m3 of dirty air but can also absorb 7572 m3 of fresh air. In addition, the middle air shaft has better ventilation performance if its inner opening is set on the top rather than on the side of the tunnel. The PM2.5 discharge efficiency of the station air shaft on the train-arriving side is 52.0~62.8%, higher than that of the middle air shaft of which the value is 26.8~40.7%. This research can provide guidance for ventilation performance evaluation of subway air shafts and provide a reference for subway tunnel air shaft location design. [ABSTRACT FROM AUTHOR]

Research on the enhanced control and emission-reduction measures to improve air quality during major events could provide data theory and scientific support for air-quality improvement during non-activities. Based on the air-quality data published by the China Environmental Monitoring Station and the meteorological elements and weather conditions released by the China Meteorological Administration, this paper explored the characteristics of air-quality evolution in Beijing from 5 August to 18 September 2015 and the weather situation during the Military Parade. The results showed that: (1) Emission-reduction measures implemented for air quality by Beijing and its surrounding area were induced, and we explored the contribution of these measures to pollutants or AQI in the locality. (2) During the 2015 Military Parade, Beijing was in the front or lower part of the high-pressure system. Due to the strong effect of North or Northeast winds, the weather situation was conducive to the diffusion of pollutants. When before or after the implementation, once the atmospheric diffusion was poor, the pollutants would accumulate gradually. Thus, it can be seen that the weather situation had a great impact on air quality. (3) During the implementation, PM2.5, PM10, NO2 and other pollutants decreased significantly, of which the concentration of PM10 decreased the most, from 109 μg·m−3 down to 34 μg·m−3, and the concentration of PM2.5 decreased by 72.73%. According to the changes between before and during the implementation or during and after the implementation, the concentration of PM10 and PM2.5 increased when the implementation of the emission-reduction measures had been finished, indicating that the enhanced control measures made a great contribution to the emission reduction in particles. (4) In addition, the annual average of AQI in the three years is 87.49, and the average value of a normal year was the average value of 2013 and 2014. The average value of the normal year during the military parade is 64.63, which was 70.40% lower than the average value of AQI during the military parade. The goal of reaching the secondary standard of GB-3095-2012 was achieved, and there was still a long way to go from the primary standard. In a few words, in order to achieve the goal of better air quality throughout the year, all parties still needed to coordinate control and make joint efforts. [ABSTRACT FROM AUTHOR]

Industrial agglomeration is a major source of regional economic development and the main pattern enterprises employ after having developed to a certain stage. Industrial agglomeration also affects the emissions of air pollutants in production. Based on provincial panel data for China from 2006 to 2019, this paper introduces the full generalized least squares (FGLS) panel econometrics model. By considering spatial correlation, the potential endogenous problem has been controlled using the instrumental variable and the effects of the co-agglomeration of manufacturing and producer services on three major air pollutants, i.e., SO2, PM2.5, and NOx, have been empirically estimated. The empirical results show that: (1) The agglomeration of manufacturing increases the emission of PM2.5 in the air, while the agglomeration of producer services and the co-agglomeration of manufacturing and producer services reduce it. Moran correlation index test showed that SO2 and NOx had no significant spatial correlation. (2) The agglomeration of manufacturing, the agglomeration of producer services, and co-agglomeration exert the most significant effects on PM2.5 in the air in central and western China. This is probably because of the availability of basic natural resources in these areas. (3) The energy consumption structure mediates the effect of the agglomeration of manufacturing on PM2.5, and human capital mediates the effect of the agglomeration of producer services on PM2.5 emissions. Based on the results, policy suggestions to improve the atmospheric environment during the process of industrial agglomeration are proposed. [ABSTRACT FROM AUTHOR]

Accurate and fine-grained individual air quality index (IAQI) prediction is the basis of air quality index (AQI), which is of great significance for air quality control and human health. Traditional approaches, such as time series, recurrent neural network or graph convolutional network, cannot effectively integrate spatial-temporal and meteorological factors and manage the dynamic edge relationship among scattered monitoring stations. In this paper, a ST-CCN-IAQI model is proposed based on spatial-temporal causal convolution networks. Both the spatial effects of multi-source air pollutants and meteorological factors were considered via spatial attention mechanism. Time-dependent features in the causal convolution network were extracted by stacked dilated convolution and time attention. All the hyper-parameters in ST-CCN-IAQI were tuned by Bayesian optimization. Shanghai air monitoring station data were employed with a series of baselines (AR, MA, ARMA, ANN, SVR, GRU, LSTM and ST-GCN). Final results showed that: (1) For a single station, the RMSE and MAE values of ST-CCN-IAQI were 9.873 and 7.469, decreasing by 24.95% and 16.87% on average, respectively. R2 was 0.917, with an average 5.69% improvement; (2) For all nine stations, the mean RMSE and MAE of ST-CCN-IAQI were 9.849 and 7.527, respectively, and the R2 value was 0.906. (3) Shapley analysis showed PM10, humidity and NO2 were the most influencing factors in ST-CCN-IAQI. The Friedman test, under different resampling, further confirmed the advantage of ST-CCN-IAQI. The ST-CCN-IAQI provides a promising direction for fine-grained IAQI prediction. [ABSTRACT FROM AUTHOR]

In this paper, a combination of simulation and testing is used to quantitatively analyze the influence of a fresh air system of indoor range hood on kitchen air quality. The evaluation criteria of kitchen air quality are established based on the air age and air exchange efficiency. The results show that, when the fresh air system is switched to on from off, the indoor mean air age is reduced to 94.7 s from 468.6 s, and the air exchange efficiency is improved to 82.4% from 16.7%. The air quality is upgraded to fresh from moderate pollution. The presented simulation analysis process can provide a guideline for in-depth study of the kitchen air quality and quantitatively assess the kitchen fresh air system. [ABSTRACT FROM AUTHOR]

Air pollution is considered one of the most significant risk factors for human health. To ensure air quality and prevent and reduce the harmful impact on human health, it is necessary to identify and measure the main air pollutants (sulfur and nitrogen oxides, PM10 and PM2.5 particles, lead, benzene, carbon monoxide, etc.), their maximum values, as well as the impact they have on mortality/morbidity rates caused by respiratory diseases. This paper aims to assess the influence of air pollution on respiratory diseases based on an analysis of principal pollutants and mortality/morbidity data sets. In this respect, four types of data are used: pollution sources inventory, air quality data sets, mortality/morbidity data at the local and national level, and clinical data of patients diagnosed with different forms of lung malignancies. The results showed an increased number of deaths caused by respiratory diseases for the studied period, correlated with the decreased air quality due to industrial and commercial activities, households, transportation, and energy production. [ABSTRACT FROM AUTHOR]

Air pollution caused by coal burning not only increases the cost of environmental pollution but also harms human health. It is urgent for China to change the practice of coal-fired central heating. Therefore, the effectiveness and sustainability of the Coal to Gas and Electricity policy have become the focus of all sectors of society. In this paper, eight cities in the Beijing–Tianjin–Hebei region were taken as the experimental groups and the other eleven cities as the control groups. Based on the PSM-DID model and the time-varying DID model, a quasi-natural experimental analysis was conducted to evaluate the effect of the policy of coal to gas and electricity to improve air quality in the Beijing–Tianjin–Hebei region from 2015 to 2020 and to test the sustainability of the policy. Three research conclusions are shown below: First, during the implementation of the policy, especially in 2019, the AQI index decreased significantly. Although there was a rebound thereafter, it was still lower than before. This shows that the Coal to Gas and Electricity policy has indeed improved the air quality in Beijing, Tianjin, and Hebei during its implementation. Second, the policy had a great impact on SO2 and PM10 but was relatively weak on PM2.5 and CO. Therefore, there is an urgent need to formulate scientific and accurate policies to control different air pollutants. Third, the time-varying DID model was used to identify the dynamic sustainability effect of the Coal to Gas and Electricity policy. The results showed that the policy had a strong impact in the initial stage, but its effect was greatly reduced at the end of the implementation or near the end, when it was far less obvious than in the initial stage of the policy. Therefore, in formulating relevant measures to reduce air pollution, it is necessary to fully consider the sustainability of the policy. [ABSTRACT FROM AUTHOR]

Aerosol Optical Depth (AOD) is one of the most important parameters of aerosol and a key physical quantity to characterize atmospheric turbidity and air pollution. Accurate retrieval of AOD is of great significance for air quality assessment. However, the spatial resolution of the currently widely used Moderate Resolution Imaging Spectroradiometer (MODIS) AOD products is too low to meet the application research of atmospheric environment at the regional scale. In 2013, China launched the Gaofen-1 (GF-1) satellite, which provides a new idea for AOD retrieval. In this paper, we apply the synergetic use of TERRA and AQUA satellite MODIS data to calculate the high-resolution AOD over Beijing based on the Synergetic Retrieval of Aerosol Properties algorithm (SRAP) and discussed scale conversion problems between AODs with different resolutions. To obtain the 100 m MODIS data, we use GF-1 wide-field-of-view data to downscale 1 km MODIS data based on mutual information method. The retrieved AOD has a spatial resolution of 100 m and can cover many land surface types. Preliminary validation was carried out with the Aerosol Robotic Network (AERONET) ground observation data. The correlation coefficient is about 0.88, and the root-mean-square error is about 0.15. Due to the high resolution of retrieved results, more detailed features can be provided in the spatial distribution. The experimental results show that the method has high precision, and further verification work is continuing. [ABSTRACT FROM AUTHOR]

In recent decades, the economy of China has developed rapidly, but this has brought widespread damage to the environment, which forces us to explore a sustainable, green, economic development model. Therefore, it is particularly necessary to clarify the relationship between economic development and environmental pollution. In this paper, we used satellite remote sensing tropospheric NO2 vertical column density (VCD) as an air quality indicator; the total exports, total imports, and industrial electricity consumption as the economic indicators; and the wind speed, temperature, and planetary boundary layer height as the meteorological factors to perform a Generalized Additive Modeling (GAM) analysis. By deducing the influence of meteorological factors, the relationship between economic indicators and the air quality indicator can be determined. When total exports increased by one billion USD (United States Dollar), the tropospheric NO2 VCDs of Nanjing and Suzhou increased by about 15% and 6%, respectively. The tropospheric NO2 VCDs of Suzhou increased by about 5% when the total imports increased by one billion USD. In addition, when the industrial electricity consumption increased by one billion kWh, the tropospheric NO2 VCDs of Nanjing, Suzhou and Xuzhou increased by about 25%, 12%, and 59%, respectively. This study provides a method to quantify the contribution of economic growth to air pollution, which is helpful for better understanding of the relationship between economic development and air quality. [ABSTRACT FROM AUTHOR]