Effective Removal of Air Pollutions by the Electrical Discharge

The objectives of this study were to understand the effects of different electrical and reactor＇s parameters on the removal efficiency of air pollution and to optimize the parameters to achieve the highest removal efficiency. The surface discharge was applied to dielectric barrier discharge reactors and the voltage, current, frequency and the electrode shape were employed to the reactor as main variables. In general, it has been known that the removal efficiency is decided by the applied power, which is calculated by multiplication of the applied voltage and current. However, the removal efficiency was significantly changed by the composition ratio of the applied voltage and current even though the experimental conditions were not changed with the same applied power. It means that the removal effectiveness should be affected not by the discharge power but by the applied voltage and current separately. From the experimental and statistical analysis, the discharge current was the most important factor to control the removal efficiency regardless of different types of the reactors and the frequency is not the direct function to control the removal rate.

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.

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%.

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.

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.

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.

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.

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.

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.

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.

Diurnal Variations of Air Pollution and Atmospheric Boundary Layer Structure in Beijing During Winter 2000/2001

The diurnal variations of gaseous pollutants and the dynamical and thermodynamic structures of the atmospheric boundary layer (ABL) in the Beijing area from January to March 2001 are analyzed in this study using data from the Beijing City Air Pollution Observation Field Experiment (BECAPEX). A heavy pollution day (22 February) and a good air quality day (24 February) are selected and individually analyzed and compared to reveal the relationships between gaseous pollutants and the diurnal variations of the ABL. The results show that gaseous pollutant concentrations exhibit a double-peak-double-valley-type diurnal variation and have similar trends but with different magnitudes at different sites in Beijing. The diurnal variation of the gaseous pollutant concentrations is closely related to (with a 1-2 hour delay of) changes in the atmospheric stability and the mean kinetic energy in the ABL.

Association between Ambient Air Pollution and Hospital Emergency Admissions for Respiratory and Cardiovascular Diseases in Beijing: a Time Series Study

Objective To investigate the association between ambient air pollution and hospital emergency admissions in Beijing. Methods In this study, a semi-parametric generalized additive model （GAM） was used to evaluate the specific influences of air pollutants （PM10, SO2, and NO2） on hospital emergency admissions with different lag structures from 2009 to 2011, the sex and age specific influences of air pollution and the modifying effect of seasons on air pollution to analyze the possible interaction. Results It was found that a 10μg/m3 increase in concentration of PMlo at lag 03 day, SO2 and NO2 at lag 0 day were associated with an increase of 0.88%, 0.76%, and 1.82% respectively in overall emergency admissions. A 10 lag/m3 increase in concentration of PM10, SO2 and NO2 at lag 5 day were associated with an increase of 1.39%, 1.56%, and 1.18% respectively in cardiovascular disease emergency admissions. For lag 02, a 10 μg/m3 increase in concentration of PM10, SO2 and NO2 were associated with 1.72%, 1.34%, and 2.57% increases respectively in respiratory disease emergency admissions. Conclusion This study further confirmed that short-term exposure to ambient air pollution was associated with increased risk of hospital emergency admissions in Beijing.

Evaluation of mercury speciation and removal through air pollution control devices of a 190 MW boiler

Air pollution control devices （APCDs） are installed at coal-fired power plants for air pollutant regulation. Selective catalytic reduction （SCR） and wet flue gas desulftLrization （FGD） systems have the co-benefits of air pollutant and mercury removal. Configuration and operational conditions of APCDs and mercury speciation affect mercury removal efficiently at coal-fired utilities. The Ontario Hydro Method （OHM） recommended by the U.S. Environmental Protection Agency （EPA） was used to determine mercury speciation simultaneously at five sampling locations through SCR-ESP-FGD at a 190 MW unit. Chlorine in coal had been suggested as a factor affecting the mercury speciation in flue gas; and low-chlorine coal was purported to produce less oxidized mercury （Hg^2＋） and more elemental mercury （Hg^0） at the SCR inlet compared to higher chlorine coal. SCR could oxidize elemental mercury into oxidized mercury when SCR was in service, and oxidation efficiency reached 71.0%. Therefore, oxidized mercury removal efficiency was enhanced through a wet FGD system. In the non-ozone season, about 89.5%-96.8% of oxidized mercury was controlled, but only 54.9%-68.8% of the total mercury was captured through wet FGD. Oxidized mercury removal efficiency was 95.9%-98.0%, and there was a big difference in the total mercury removal efficiencies from 78.0% to 90.2% in the ozone season. Mercury mass balance was evaluated to validate reliability of OHM testing data, and the ratio of mercury input in the coal to mercury output at the stack was from 0.84 to 1.08.

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.

A Time Series Analysis of Outdoor Air Pollution and Preterm Birth in Shanghai, China

Objective To investigate the relation between air pollution exposure and preterm birth in Shanghai, China. Methods We examined the effect of ambient air pollution on preterm birth using time-series approach in Shanghai in 2004. This method can eliminate potential confounding by individual risk factors that do not change over a short period of time. Daily numbers of preterm births were obtained from the live birth database maintained by Shanghai Municipal Center of Disease Control and Prevention. We used the generalized additive model （GAM） with penalized splines to analyze the relation between preterm birth, air pollution, and covariates. Results We observed a significant effect of outdoor air pollution only with 8-week exposure before preterm births. An increase of 10 μg/m^3 of 8-week average PM10, SO2, NO2, and O3 corresponded to 4.42% （95%CI 1.60%, 7.25%）, 11.89% （95%CI 6.69%, 17.09%）, 5.43% （95%CI 1.78%, 9.08%）, and 4.63% （95%CI 0.35%, 8.91%） increase of preterm birth. We did not find any significant acute effect of outdoor air pollution on preterm birth in the week before birth. Conclusion Ambient air pollution may contribute to the risk of preterm birth in Shanghai. Our analyses also strengthen the rationale for further limiting air pollution level in the city.

Statistical Distributions of Ambient Air Pollutants in Shanghai, China

Objective To determine the best statistical distribution of concentration data of major air pollutants in Shanghai. Methods Four types of theoretic distributions (lognormal, gamma, Pearson V and extreme value) were chosen to fit daily average concentration data of PM10, SO2 and NO2 from June 1, 2000 to May 31, 2003 in Shanghai by using the maximum likelihood method. The fit results were evaluated by Chi-square test. Results The best-fit distributions for PM10,SO2 and NO2 concentrations in Shanghai were lognormal, Pearson V, and extreme value distributions, respectively. Conclusion The results can be further applied to local air pollution prediction and control, e.g., the probabilities exceeding the air quality standard and emission source reduction of air pollutant concentration to meet the standard.

Tobacco, air pollution, environmental carcinogenesis, and thoughts on conquering strategies of lung cancer

Each year there will be an estimated 2.1 million new lung cancer cases and 1.8 million lung cancer deaths worldwide.Tobacco smoke is the No.1 risk factors of lung cancer,accounting for>85%lung cancer deaths.Air pollution,or haze,comprises ambient air pollution and household air pollution,which are reported to cause 252,000 and 304,000 lung cancer deaths each year,respectively.Tobacco smoke and haze(hereafter,smohaze)contain fine particles originated from insufficient combustion of biomass or coal,have quite similar carcinogens,and cause similar diseases.Smohaze exert hazardous effects on exposed populations,including induction of a large amount of mutations in the genome,alternative splicing of mRNAs,abnormalities in epigenomics,initiation of tumor-promoting chronic inflammation,and facilitating immune escape of transformed cells.Tackling smohaze and development of multi-targets-based preventive and therapeutic approaches targeting smohaze-induced carcinogenesis are the key to conquer lung cancer in the future.

Short-term effects of air pollution on acute myocardial infarctions in Shanghai, China, 2013-2014

Background Although particulate matter, with diameters 〈 2.5 μm （PM2.5） and 〈 10 μm （PM10）, and other pollutants have been associated with cardiovascular morbidity and mortality, the effect of pollutants on acute myocardial infarctions （AMIs） has rarely been investigated in Asia, especially in Shanghai, China. Methods Between 1 November 2013 and 27 April 2014, 972 patients from the Pudong District, Shanghai City, were assessed by the Emergency Medical Service. A case-crossover design was used to analyze exposure to air pollution and the AMI risk. Exposures to PM2.5, PM10, nitrogen dioxide （NO2）, sulphurdioxide （SO2）, and carbon monoxide （CO） were based on the mean urban background levels. The associations among AMI admissions, the included pollutants, temperature, and relative humidity were analyzed using correlation and logistic regression. Results The urban background levels of PM2.5, PM10 and CO were associated with an increased risk of AMI, unlike NO2 and SO2 levels. The OR （95% CI） for AMI were 1.16 （1.03-1.29）, 1.05 （1.01-1.16）, 0.82 （0.75-1.02）, 0.87 （0.63-1.95）, and 1.08 （1.02-1.21） for PM2.5, PM10, NO2, SO2, and CO, respectively. Increases in the air quality index （AQI） were associated with more AMI occurrences. There was no correlation between fluctuations in temperature and relative humidity with AMI hospital admissions. Conclusions Short-term exposure to moderate-serious pollution levels is associated with increased risk of AMI. Increased PM2.5, PM10 and CO levels are related to increased AMI admissions.