Short-term Effects of Air Pollution on Mortality in a Heavily Polluted Chinese City

Since the London fog in 1952, numerous epidemioLogical studies have revealed that both short-term and long-term exposure to air pollutants is associated with the development of diseases[1]. Up to date, the assessment of air quality on health and air quality standard establishment in developing countries were mainly relied on extrapolation based on the results from long-term cohort studies conducted in Europe and North America.

Clarifying the relationship between PM2.5 and ozone complex pollution and synoptic patterns in a typical petrochemical city in the Bohai Rim region of China:Implications for air pollution forecasting and control

Meteorological conditions are vital to PM_(2.5)and ozone(O_(3))complex pollution.Herein,the T-mode principal com-ponent analysis method was employed to objectively classify the 925-hPa geopotential height field of Dongying from 2017 to 2022.Synoptic patterns associated with four pollution types-namely,PM_(2.5)-only pollution,O_(3)-only pollution,Co-occurring of PM_(2.5)and O_(3)pollution,Non-occurring of PM_(2.5)and O_(3)pollution-were characterized at different time scales.The results indicated that synoptic classes conducive to PM_(2.5)-only pollution were“high-pressure top front”,“offshore high-pressure rear”,and“high-pressure inside”,while those conducive to O_(3)-only pollution were“offshore high-pressure rear”,“subtropical high”,and“high and low systems”.The Co-occurring of PM_(2.5)and O_(3)pollution were influenced by high pressure,and the Non-occurring of PM_(2.5)and O_(3)pollution were linked to precipitation and strong northerly winds.The variation in dominant synoptic patterns is crucial in the frequency changes of the four pollution types,which was further validated through the analysis of typical cases.Under the favorable meteorological conditions of high-pressure control with strong northerly winds or a subtropical high and inverted trough both with strong precipitation,there is potential to achieve coordinated control of PM_(2.5)and O_(3)in Dongying.Additionally,measures like artificially manipulating local humidity could be adopted to alleviate pollution levels.This study reveals the importance of comprehending the meteorological factors contributing to the formation of PM_(2.5)and O_(3)complex pollution for the improvement of urban air quality in the Bohai Rim region of China when emissions are high and the concentration of air pollutants exhibits high meteorological sensitivity.

Long-Term Exposure to Low-Level Ambient Air Pollution and Mortality among 0.3 Million Chinese Older Adults

Objective Evidence that long-term exposure to ambient air pollution increases mortality among older adults,particularly those residing in low-level air pollution locations,remains scarce.This study investigated the potential links between long-term low-level air pollution exposure and mortality among Chinese older adults.Methods A population-based study with 317,464 individuals aged≥65 years was conducted in Shenzhen,China during 2018 and 2020.Logistic regression models were used to analyze the associations between long-term exposure to air pollution and all-cause mortality,as the primary outcome,as well as non-accidental,cancer and cardiovascular mortality.Results Significant associations of PM1,PM_(2.5),PM_(10),SO_(2),CO,and O3 exposures with a higher risk of all-cause mortality were found.Adjusted odds ratio(OR)for each 1μg/m^(3) increment was 1.49[95%confidence interval(CI):1.46,1.53]for PM1,1.30(1.27,1.32)for PM_(2.5),1.05(1.04,1.06)for PM_(10),5.84(5.39,6.32)for SO_(2),1.04(1.04,1.05)for CO,and 1.02(1.00,1.03)for O3,respectively.Long-term PM1,PM_(2.5),PM_(10),SO_(2),and CO exposures also elevated the risks of non-accidental,cancer and cardiovascular mortality.Conclusion Long-term low-level air pollution exposure was associated with an increased mortality risk among Chinese older adults.

Impact of air pollution on urbanization:evidence at China’s city level

This paper investigates the effect and transmission mechanism of air pollution on urbanization based on data from China’s 107 cities during 2005–2018.In order to identify the impact of air pollution on China’s urbanization,we utilized night light data to represent the level of urbanization and used temperature inversion as an instrumental variable to mitigate endogeneity within the two-stage least squares framework.The results suggest that air pollution significantly slowed China’s urbanization process with economic growth acting as the transmission mechanism.The heterogeneity analyses revealed that air pollution had a greater negative impact on urbanization in northern regions than that in southern regions,and a greater negative impact in resource-oriented cities than that in non-resource-based cities.We also find that air pollution was to the detriment of urbanization in larger cities,which have more than 3 million residents,while it did not have a significant impact on Type II large cities,which have fewer than 3 million residents.

The Influence of Air Pollution Concentrations on Solar Irradiance Forecasting Using CNN-LSTM-mRMR Feature Extraction

Maintaining a steady power supply requires accurate forecasting of solar irradiance,since clean energy resources do not provide steady power.The existing forecasting studies have examined the limited effects of weather conditions on solar radiation such as temperature and precipitation utilizing convolutional neural network(CNN),but no comprehensive study has been conducted on concentrations of air pollutants along with weather conditions.This paper proposes a hybrid approach based on deep learning,expanding the feature set by adding new air pollution concentrations,and ranking these features to select and reduce their size to improve efficiency.In order to improve the accuracy of feature selection,a maximum-dependency and minimum-redundancy(mRMR)criterion is applied to the constructed feature space to identify and rank the features.The combination of air pollution data with weather conditions data has enabled the prediction of solar irradiance with a higher accuracy.An evaluation of the proposed approach is conducted in Istanbul over 12 months for 43791 discrete times,with the main purpose of analyzing air data,including particular matter(PM10 and PM25),carbon monoxide(CO),nitric oxide(NOX),nitrogen dioxide(NO_(2)),ozone(O₃),sulfur dioxide(SO_(2))using a CNN,a long short-term memory network(LSTM),and MRMR feature extraction.Compared with the benchmark models with root mean square error(RMSE)results of 76.2,60.3,41.3,32.4,there is a significant improvement with the RMSE result of 5.536.This hybrid model presented here offers high prediction accuracy,a wider feature set,and a novel approach based on air concentrations combined with weather conditions for solar irradiance prediction.

Impact of Air Pollutants on Lung Function and Inflammatory Response in Asthma in Shanghai

Objective Air pollution is a leading public health issue.This study investigated the effect of air quality and pollutants on pulmonary function and inflammation in patients with asthma in Shanghai.Methods The study monitored 27 asthma outpatients for a year,collecting data on weather,patient self-management[daily asthma diary,peak expiratory flow(PEF)monitoring,medication usage],spirometry and serum markers.To explore the potential mechanisms of any effects,asthmatic mice induced by ovalbumin(OVA)were exposed to PM_(2.5).Results Statistical and correlational analyses revealed that air pollutants have both acute and chronic effects on asthma.Acute exposure showed a correlation between PEF and levels of ozone(O_(3))and nitrogen dioxide(NO_(2)).Chronic exposure indicated that interleukin-5(IL-5)and interleukin-13(IL-13)levels correlated with PM_(2.5)and PM_(10)concentrations.In asthmatic mouse models,exposure to PM_(2.5)increased cytokine levels and worsened lung function.Additionally,PM_(2.5)exposure inhibited cell proliferation by blocking the NF-κB and ERK phosphorylation pathways.Conclusion Ambient air pollutants exacerbate asthma by worsening lung function and enhancing Th2-mediated inflammation.Specifically,PM_(2.5)significantly contributes to these adverse effects.Further research is needed to elucidate the mechanisms by which PM_(2.5)impacts asthma.

Generation of a Controlled Polluted Atmosphere for Vehicles’ Cabins Air Quality Characterization

Air pollution induces significant health risks to individuals exposed to high levels of pollutants concentration. For ground vehicles, pollutants infiltrate the car cabin through the ventilation system, leading to potential health issues. To address this problem, a project was undertaken to develop a protocol for characterizing in-cabin air quality. The study involved a closed chamber (the bubble) where its internal multiphase flow has been optimized to create controlled polluted atmospheres. Experiments were conducted to optimize the positioning of the stirring fan and particle generation source, ensuring a homogeneous distribution of fine and ultrafine particles. This study demonstrated the feasibility of implementing a platform dedicated to characterizing the vehicles’ in-cabin air quality under controlled conditions. It allows a better understanding of the dynamics of particle infiltration and the establishment of an optimized protocol for simultaneous measurements of indoor and outdoor concentrations.

Climate change,ambient air pollution,and students'mental health

The impact of global climate change and air pollution on mental health has become a crucial public health issue.Increased public awareness of health,advancements in medical diagnosis and treatment,the way media outlets report environmental changes and the variation in social resources affect psychological responses and adaptation methods to climate change and air pollution.In the context of climate change,extreme weather events seriously disrupt people's living environments,and unstable educational environments lead to an increase in mental health issues for students.Air pollution affects students'mental health by increasing the incidence of diseases while decreasing contact with nature,leading to problems such as anxiety,depression,and decreased cognitive function.We call for joint efforts to reduce pollutant emissions at the source,improve energy structures,strengthen environmental monitoring and governance,increase attention to the mental health issues of students,and help student groups build resilience;by establishing public policies,enhancing social support and adjusting lifestyles and habits,we can help students cope with the constantly changing environment and maintain a good level of mental health.Through these comprehensive measures,we can more effectively address the challenges of global climate change and air pollution and promote the achievement of the United Nations Sustainable Development Goals.

Ensemble Deep Learning Based Air Pollution Prediction for Sustainable Smart Cities

Big data and information and communication technologies can be important to the effectiveness of smart cities.Based on the maximal attention on smart city sustainability,developing data-driven smart cities is newly obtained attention as a vital technology for addressing sustainability problems.Real-time monitoring of pollution allows local authorities to analyze the present traffic condition of cities and make decisions.Relating to air pollution occurs a main environmental problem in smart city environments.The effect of the deep learning(DL)approach quickly increased and penetrated almost every domain,comprising air pollution forecast.Therefore,this article develops a new Coot Optimization Algorithm with an Ensemble Deep Learning based Air Pollution Prediction(COAEDL-APP)system for Sustainable Smart Cities.The projected COAEDL-APP algorithm accurately forecasts the presence of air quality in the sustainable smart city environment.To achieve this,the COAEDL-APP technique initially performs a linear scaling normalization(LSN)approach to pre-process the input data.For air quality prediction,an ensemble of three DL models has been involved,namely autoencoder(AE),long short-term memory(LSTM),and deep belief network(DBN).Furthermore,the COA-based hyperparameter tuning procedure can be designed to adjust the hyperparameter values of the DL models.The simulation outcome of the COAEDL-APP algorithm was tested on the air quality database,and the outcomes stated the improved performance of the COAEDL-APP algorithm over other existing systems with maximum accuracy of 98.34%.

Analysis of Characteristics and Meteorological Influencing Factors of Air Pollution in Luojiang District,Deyang City

Based on the monitoring data of ambient air quality and meteorological observation data,the characteristics and meteorological influencing factors of air pollution in Luojiang District of Deyang City from 2018 to 2022 were analyzed.The results show that from 2018 to 2022,the main air pollutants affecting the air quality of Luojiang District of Deyang City were PM_(2.5) and PM_(10),and the primary pollutant on heavy pollution days was basically PM_(2.5).PM_(2.5) and PM_(10) pollution showed obvious seasonal differences,and PM_(2.5) concentration exceeded the limit mainly in spring and winter,among which it was the most serious in early spring,especially in January and February,followed by December.PM_(10) exceeding the standard had a high seasonal correlation with PM_(2.5) exceeding the standard,mainly in spring and winter,among which it was the most serious in winter,especially in December,followed by January.PM_(2.5) and PM_(10) pollution showed an overall weakening trend.PM_(2.5) and PM_(10) concentration were closely related to meteorological factors such as temperature,relative humidity,wind speed,precipitation and air pressure,and were mainly affected by rainfall.

Ultra-Net Emission and Solid Waste Transfer of Air Pollutants in Steel, Power and Cement Industries—The Idea of Construction and Key Technology of Denitration Engineering Transformation and By-Product Formation

In this paper, an integrated desulfurization and denitrification technology is proposed for ultra-low emissions of SO2 and NOx in the steel, power and cement industries. A cost-effective and operationally efficient control strategy is realized through a forced oxidation-absorption-reduction process, which reduces equipment investment and operating costs. The technology was adapted to continuous and intermittent denitrification in different temperature zones, promoting the recycling of desulfurization and denitrification products. The study also explored the use of a highly active absorbent obtained by the hydration reaction of coal ash and lime from a power company for the desulfurization and denitrification of sintered flue gases in iron and steel mills, which produces by-products that can be used as retarding agents in the cement industry, resulting in a circular economy. The article emphasizes the importance of improving the lime digestion process and developing new denitrification agents for environmentally safe and cost-effective flue gas treatment.

Mapping Air Quality Using Remote Sensing Technology: A Case Study of Nairobi County

Nairobi County experiences rapid industrialization and urbanization that contributes to the deteriorating state of air quality, posing a potential health risk to its growing population. Currently, in Nairobi County, most air quality monitoring stations use low-cost, inaccurate monitors prone to defects. The study’s objective was to map Nairobi County’s air quality using freely available remotely sensed imagery. The Air Pollution Index (API) formula was used to characterize the air quality from cloud-free Landsat satellite images i.e., Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 OLI from Google Earth Engine. The API values were computed based on vegetation indices namely NDVI, TVI, DVI, and the SWIR1 and NIR bands on the QGIS platform. Qualitative accuracy assessment was done using sample points drawn from residential, industrial, green spaces, and traffic hotspot categories, based on a passive-random sampling technique. In this study, Landsat 5 API imagery for 2010 provided a reliable representation of local conditions but indicated significant pollution in green spaces, with recorded values ranging from -143 to 334. The study found that Landsat 7 API imagery in 2002 showed expected results with the range of values being -55 to 287, while Landsat 8 indicated high pollution levels in Nairobi. The results emphasized the importance of air quality factors in API calibration and the unmatched spatial coverage of satellite observations over ground-based monitoring techniques. The study recommends the recalibration of the API formula for characteristic regions, exploring newer satellite sensors like those onboard Landsat 9 and Sentinel 2, and involving key stakeholders in a discourse to develop a suitable Kenyan air quality index.

Enhancing Patients Outcomes and Infection Control through Smart Indoor Air Quality Monitoring Systems

Air pollution poses a critical threat to public health and environmental sustainability globally, and Nigeria is no exception. Despite significant economic growth and urban development, Nigeria faces substantial air quality challenges, particularly in urban centers. While outdoor air pollution has received considerable attention, the issue of indoor air quality remains underexplored yet equally critical. This study aims to develop a reliable, cost-effective, and user-friendly solution for continuous monitoring and reporting of indoor air quality, accessible from anywhere via a web interface. Addressing the urgent need for effective indoor air quality monitoring in urban hospitals, the research focuses on designing and implementing a smart indoor air quality monitoring system using Arduino technology. Employing an Arduino Uno, ESP8266 Wi-Fi module, and MQ135 gas sensor, the system collects real-time air quality data, transmits it to the ThingSpeak cloud platform, and visualizes it through a user-friendly web interface. This project offers a cost-effective, portable, and reliable solution for monitoring indoor air quality, aiming to mitigate health risks and promote a healthier living environment.

Remote Sensing and Geospatial Approach for Assessing the Impact of Automobiles on Air Quality, Case Study: Casablanca

Urban air pollution is a major challenge facing rapidly growing cities in the Middle East and North Africa (MENA) region, with vehicle emissions being a significant contributor. This study aims to analyze the spatial and temporal patterns of air pollutants, particularly nitrogen dioxide (NO2), in Casablanca, Morocco, and investigate the relationship with urban development and transportation characteristics. By integrating satellite remote sensing data and Google Earth Engine (GEE) techniques, we provide a comprehensive assessment of air quality in Casablanca and demonstrate the value of using geospatial approaches for informing policymakers and urban planners. The results highlight seasonal variations in NO2 levels, the identification of pollution hotspots, and the quantification of the influence of urban features and traffic on air quality. We discuss the implications of these findings for targeted interventions to improve air quality and the potential for expanding the methodology to other pollutants and cities in the region.

Characteristics of Fluoride Contents in Plants and Soils in Kaili City Under Air Pollution

Fluoride contents in plants and soils in Kaili City were measured with fluorinion as per electrode method and the related characteristics were analyzed in order to explore effects of air fluoride pollution on plant and soil.The results indicated that fluoride content in plants tended to be volatile in 135.62-1 420.97 μg/g and averaged 513.99 μg/g;fluoride content in soils changed from 240.50-340.36 μg/g and averaged 279.60 μg/g.The contents of plant and soil both exceeded background value,suggesting that plants and soils in the region have been polluted.In addition,fluoride contents differ significantly upon plants.In detail,the maximal content was in Camelliaolelfera Abel and the minimal in Camelliaolelfera Abel.The contents of fluoride in different plant species vary,as follows：shrub vine herbaceous plant arbor;evergreen plants deciduous plant;fluoride contents in plants and soils also differ in varying degrees upon research sites.

Industrial solid wastes to environmental protection materials for removal of gaseous pollutants:A review

The application of industrial solid wastes as environmentally functional materials for air pollutants control has gained much attention in recent years due to its potential to reduce air pollution in a cost-effective manner.In this review,we investigate the development of industrialwaste-based functional materials for various gas pollutant removal and consider the relevant reaction mechanism according to different types of industrial solid waste.We see a recent effort towards achieving high-performance environmental functional materials via chemical or physical modification,in which the active components,pore size,and phase structure can be altered.The review will discuss the potential of using industrial solid wastes,these modified materials,or synthesized materials from raw waste precursors for the removal of air pollutants,including SO_(2),NO_(x),Hg^(0),H_(2)S,VOCs,and CO_(2).The challenges still need to be addressed to realize this potential and the prospects for future research fully.The suggestions for future directions include determining the optimal composition of these materials,calculating the real reaction rate and turnover frequency,developing effective treatment methods,and establishing chemical component databases of raw industrial solid waste for catalysts/adsorbent preparation.

An improved GCN−TCN−AR model for PM_(2.5) predictions in the arid areas of Xinjiang,China

As one of the main characteristics of atmospheric pollutants,PM_(2.5) severely affects human health and has received widespread attention in recent years.How to predict the variations of PM_(2.5) concentrations with high accuracy is an important topic.The PM_(2.5) monitoring stations in Xinjiang Uygur Autonomous Region,China,are unevenly distributed,which makes it challenging to conduct comprehensive analyses and predictions.Therefore,this study primarily addresses the limitations mentioned above and the poor generalization ability of PM_(2.5) concentration prediction models across different monitoring stations.We chose the northern slope of the Tianshan Mountains as the study area and took the January−December in 2019 as the research period.On the basis of data from 21 PM_(2.5) monitoring stations as well as meteorological data(temperature,instantaneous wind speed,and pressure),we developed an improved model,namely GCN−TCN−AR(where GCN is the graph convolution network,TCN is the temporal convolutional network,and AR is the autoregression),for predicting PM_(2.5) concentrations on the northern slope of the Tianshan Mountains.The GCN−TCN−AR model is composed of an improved GCN model,a TCN model,and an AR model.The results revealed that the R2 values predicted by the GCN−TCN−AR model at the four monitoring stations(Urumqi,Wujiaqu,Shihezi,and Changji)were 0.93,0.91,0.93,and 0.92,respectively,and the RMSE(root mean square error)values were 6.85,7.52,7.01,and 7.28μg/m^(3),respectively.The performance of the GCN−TCN−AR model was also compared with the currently neural network models,including the GCN−TCN,GCN,TCN,Support Vector Regression(SVR),and AR.The GCN−TCN−AR outperformed the other current neural network models,with high prediction accuracy and good stability,making it especially suitable for the predictions of PM_(2.5)concentrations.This study revealed the significant spatiotemporal variations of PM_(2.5)concentrations.First,the PM_(2.5) concentrations exhibited clear seasonal fluctuations,with higher levels typically observed in winter and differences presented between months.Second,the spatial distribution analysis revealed that cities such as Urumqi and Wujiaqu have high PM_(2.5) concentrations,with a noticeable geographical clustering of pollutions.Understanding the variations in PM_(2.5) concentrations is highly important for the sustainable development of ecological environment in arid areas.

Progress of Air Pollution Control in China and Its Challenges and Opportunities in the Ecological Civilization Era

China’s past economic growth has substantially relied on fossil fuels,causing serious air pollution issues.Decoupling economic growth and pollution has become the focus in developing ecological civilization in China.We have analyzed the three-decade progress of air pollution controls in China,highlighting a strategic transformation from emission control toward air quality management.Emission control of sulfur dioxide(SO2)resolved the deteriorating acid rain issue in China in 2007.Since 2013,control actions on multiple precursors and sectors have targeted the reduction of the concentration of fine particulate matter(PM2.5),marking a transition to an air-quality-oriented strategy.Increasing ozone(O3)pollution further requires O3 and PM2.5 integrated control strategies with an emphasis on their complex photochemical interactions.Fundamental improvement of air quality in China,as a key indicator for the success of ecological civilization construction,demands the deep de-carbonization of China’s energy system as well as more synergistic pathways to address air pollution and global climate change simultaneously.

Evaluation of ambient air quality in Guangzhou, China

On the basis of the reported air quality index （API） and air pollutant monitoring data provided by the Guangzhou Environment Monitoring Stations over the last twenty-five years, the characteristics of air quality, prominent pollutants, and variation of the average annual concentrations of SOE, NOE, total suspended particulate （TSP）, fine particulates （PM10）, CO and dustfall in Guangzhou City were analyzed. Results showed that TSP was the prominent pollutant in the ambient air environment of Guangzhou City. Of the prominent pollutants, TSP accounted for nearly 62%, SOE 12.3%, and NOx 6.4%, respectively. The average API of Guangzhou over 6 years was higher than that of Beijing, Tianjin, Nanjing, Hangzhou, Suzhou and Shanghai, and lower than that of Shenzhen, Zhuhai and Shantou. Concentrations of air pollutants have shown a downward trend in recent years, but they are generally worse than ambient air quality standards for USA, Hong Kong and EU. SOE and NOx pollution were still serious, impling that waste gas pollution from all kinds of vehicles had become a significant problem for environmental protection in Guangzhou. The possible causes of worsening air quality were also discussed in this paper.

STUDY ON HUMAN EXPOSURE ASSESSMENT TO INDOOR/OUTDOOR AIR POLLUTANTS

The environmental air quality is one of the important problems of people′s concern. The study on human exposure to air pollutants provides an important basis for the nuprorement of human health effects. The questionnaire about time activity patterns includes questions about time spent in the houses, personal activities, indoor combustion sources, information about hobbies, (e.g. smoking), home repairing and decorating materials, and personal product use and so on. The questionnaire was conducted with 662 respondents over 16 years of age in Tianjin, China from fall 1995 to summer 1996. The results of the investigation show that 84% of people′s activities happen indoors, and 58% in family. The field researches of air pollutants monitoring of the most important air pollutants either indoors or outdoors, including carbon monoxide (CO), formaldehyde (HCHO), inhaled particulate (IR), and Benzpyrene(Bap), were carried out in winter and summer from 1995 to 1996. The results of the study on human exposure to four selected air pollutants show that indoor sources and their air pollution cause relatively high exposure in the total air pollution exposures. For example, only the exposure indoors at home is 71.0%,60.3%, 69.4% and 74.0% respectively for CO, HCHO, IR and Bap in the total air pollution exposure.