Seasonal Influence in Traffic-Related Air Pollutants Concentrations in Urban Parks from Porto Alegre,Brazil

Traffic-related air pollution is an alarming source of pollutants exposure and consequently to the development of several adverse health effects. Otherwise, green spaces are reported to improve health status. Although, in an urban scenario most of these areas are located near air pollutants sources, as vehicle fleet. Thus, the aim of the present study was to determine, during one year, the levels of nitrogen dioxide (NO2) and ozone (O3) in the main parks from Porto Alegre—Brazil. This study focused on three urban parks: Germania, Moinhos de Vento and Marinha do Brasil Park. Nitrogen dioxide and ozone measurements were accessed by passive monitoring in four campaigns including all seasons and performed at distances of 0 m, 15 m, 30 m, 45 m, 60 m and 75 m from the main road at each park. NO2 and O3 concentration among the parks was not different (p > 0.05), as well as the mean concentration of NO2 and O3 of all parks in the six sites did not differ (p > 0.05). However, season 1 and 3 showed increased NO2 and O3 concentration. Temperature were decreased in season 1 and 3 (p p > 0.05). Traffic flow was higher in Moinhos de Vento Park and Marinha do Brasil Park compared to Germania Park (p 2 and O3 concentration in urban parks from Porto Alegre.

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.

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.

Estimating Exposure to Traffic-Related Air Pollution and Its Consequences on Respiratory Health in Population Working or Living along the Trunk Road: A Systematic Review

Introduction: Urban Air pollution is increasingly becoming a major health and sustainable development issue. Several studies showed that Traffic-related air pollution (TRAP) is one of the main sources of urban air pollution and has serious consequences on respiratory health. As no systematic review focused on the traffic-related air pollution and respiratory health in the target population of individuals working in a shop or in an office or individuals living along the trunk road, the authors conducted the current study to try to fill this gap. Methods: A systematic review search was conducted using MEDLINE (PubMed), Scientific Research Publishing: SCIRP, Web of Science, Google scholar. Studies were included if they meet the following selection criteria: 1) focus on population working or living along a major/trunk road; 2) studies had reported clearly at least on the exposure variables related to TRAP; 3) the association between TRAP and development of respiratory symptoms or respiratory diseases was established. Results: 13 articles were selected on the 192 articles that were retrieved in the initial research. Exposure to traffic-related air pollution was determined by using distance to road, traffic intensity and pollutants measured. The main respiratory health problems found were cough, wheeze, asthma and bronchitis. No article discussed about roundabouts in characterizing exposure to traffic-related air pollution. Conclusion: Distance to road, traffic density and pollutants measured are the usual methods to characterize the exposure to traffic-related air pollution and its consequences on respiratory health. Regarding the context of area occupations in African cities, it is necessary to focus on population around roundabouts and see if they are not more exposed to TRAP.

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.

Spatiotemporal Variations and Regional Transport of Air Pollutants in Two Urban Agglomerations in Northeast China Plain

Characteristics of air pollution in Northeast China(NEC) received less research attention in the past comparing to other heavily polluted regions in China.Spatiotemporal variations of six criteria air pollutants(PM10, PM2.5, SO2, NO2, O3 and CO) in Central Liaoning Urban Agglomeration(CLUA) and Harbin-Changchun Urban Agglomeration(HCUA) in NEC Plain were analyzed in this study based on three-year hourly observations of air pollutants and meteorological variables from 2015 to 2017.The results indicated that the annual mean concentrations of air pollutants are generally higher in the middle and southern regions in NEC Plain and lower in the northern region.Megacities such as Shenyang, Harbin and Changchun experience severe air pollution, with a three-year averaged air quality index(AQI) larger than 80, far exceeding the daily AQI standard at the first-level of 50 in China.The annual mean PM and SO2 concentrations decrease most significantly in NEC urban agglomerations from 2015 to 2017, followed by CO and NO2, while O3 shows a slight increasing trend.All the six pollutants exhibit obvious seasonal and diurnal variations, and these variations are dictated by local emission and meteorological conditions.PM2.5 and O3 concentrations in NEC urban agglomerations strongly depend on wind conditions.High O3 concentrations at different cities usually occur in presence of strong winds but are independent on wind direction(WD), while high PM2.5 is usually accompanied by weak winds and poor dispersion condition, and sometimes also occur when the northerly or southerly winds are strong.Regional transport of air pollutants between NEC urban agglomerations is common.A severe haze event on November 1–4, 2017 is examined to demonstrate the role of regional transport on pollution.

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.

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.

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.

Serum Clara Cell Protein（CC16）：A New Valid Marker of the Distal Airway Damages Caused by Air Pollutants in Rats

Cell count, LDH, total protein, Clara cell protein (CC16) and lysozyme in bronchoalveolar lavage fluid (BAL) and serum were determined in rats. 30 - 50 ppm sulfur dioxide for 4 h, 12 h and 2 week exposure didn't produce any change of above parameters. 1. 0 ppm, 0. 5 ppm, 0. 38 ppm, 0. 25 ppm and 0. 20 ppm ozone for 7 h exposure tests were performed. CC16, total protein and LDH in BAL had significantly changes in BAL in ozone groups. Only serum CC16 showed significant increase among ozone groups. Both BAL and serum CC16 were of the best exposure-response relationship with ozone concentration.The change of BAL fluid reached high peak 18-24 h after ozone exposure.Serum CC16 seemed to be a valid marker of the distal airway damages caused by air pollutants.

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%.

Air pollutants and greenhouse gases emission inventory for power plants in the Antarctic

Emissions of air pollutants and greenhouse gases into the atmosphere in Antarctica from power plants with diesel generators(the main sources of energy at Antarctic research stations and the main stationary sources of anthropogenic emissions in the Antarctic)were assessed.A bottom-up approach was used to compile an emission inventory for the Antarctic.This involved estimating emissions at various spatial levels by sequentially aggregating estimate emissions from point emission sources.This is the first time this approach has been proposed and used.Emissions of CO2,NOx,particulate matter(PM10),and CO in the modern period were estimated at the research station,geographic region,natural domain,biogeographic region,continent section,and whole continent scales.Yearly emissions are presented here,but the approach allows emissions at different averaging periods to be estimated.This means mean or maximum yearly,monthly,daily,or hourly emissions can be estimated.The estimates could be used to model pollutant transmission and dispersion,assess the impacts of pollutants,and develop emission forecasts for various scenarios.

Changes in air pollutants during the COVID-19 lockdown in Beijing:Insights from a machine-learning technique and implications for future control policy

The COVID-19 lockdowns led to abrupt reductions in human-related emissions worldwide and had an unintended impact on air quality improvement.However,quantifying this impact is difficult as meteorological conditions may mask the real effect of changes in emissions on the observed concentrations of pollutants.Based on the air quality and meteorological data at 35 sites in Beijing from 2015 to 2020,a machine learning technique was applied to decouple the impacts of meteorology and emissions on the concentrations of air pollutants.The results showed that the real(“deweathered”)concentrations of air pollutants(expect for O 3)dropped significantly due to lockdown measures.Compared with the scenario without lockdowns(predicted concentrations),the observed values of PM_(2.5),PM_(10),SO_(2),NO_(2),and CO during lockdowns decreased by 39.4%,50.1%,51.8%,43.1%,and 35.1%,respectively.In addition,a significant decline for NO_(2)and CO was found at the background sites(51%and 37.8%)rather than the traffic sites(37.1%and 35.5%),which is different from the common belief.While the primary emissions reduced during the lockdown period,episodic haze events still occurred due to unfavorable meteorological conditions.Thus,developing an optimized strategy to tackle air pollution in Beijing is essential in the future.

The Influence of Atmospheric Parameters on Production and Distribution of Air Pollutants in Bayelsa: A State in the Niger Delta Region of Nigeria

Air pollution is a primary environmental problem in the Niger Delta region of Nigeria due to oil spills including the gas emissions associated with industrial effluents. However, a good understanding and quantification of atmospheric parameters (wind speed, wind direction, temperature, relative humidity, solar radiation and cloud cover) that influence air pollution (CH4, NO2 and O3) concentrations in this region could assist in the mitigation and distribution of these pollutants. This work examines the influence of atmospheric parameters on the production and distribution of air pollutants in the Niger Delta region of Nigeria for the development of control strategies that will enhance the mitigation and amelioration of the significant impacts that these atmospheric pollutants could have on the populace in this part of the country. The CH4 and NO2 data utilized in this study were sourced from the European Space Agency (ESA), while that of tropospheric ozone (O3) was obtained from the National Aeronautics and Space Administration (NASA), and the atmospheric parameters data were provided by the Nigeria Meteorological Agencies (NIMET), Lagos. The analysis of the daily pollutants (CH4, NO2 and O3) including the atmospheric parameters in this region of the Niger Delta for the period 2003 to 2010 was carried out using standard statistical approach including the graphical method, stepwise regression model, least-square method, and correlation analysis. The Mann-Kendal rank statistics was also utilized in identifying the meaningful long-term trends, validation and testing of the homogeneity of the concentrations of the pollutants. The results of the correlations of CH4, NO2 and O3 concentrations with their previous day’s concentrations showed a strong significance in regression analysis for both CH4 and O3. The coefficient of determination of CH4 and O3 was obtained as 0.654 and 0.810 respectively, while a very weak correlation was obtained for NO2. However, despite that a very strong negative correlation of -0.809 and -0.900 was obtained between wind speed and both the CH4 and O3 pollutants respectively, a moderate correlation was obtained between the wind speed and NO2. This implies that amongst the atmospheric parameters considered in this study for the region of the Niger Delta in Nigeria, wind speed has much influence on the variation of both CH4 and O3 concentrations, but with a little influence on the NO2 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.

Bivariate Analysis of Pollutants Monthly Maxima in Mexico City Using Extreme Value Distributions and Copula

In the present work, we are interested in studying the joint distributions of pairs of the monthly maxima of the pollutants used by the environmental authorities in Mexico City to classify the air quality in the metropolitan area. In order to obtain the joint distributions a copula will be considered. Since we are analyzing the monthly maxima, the extreme value distributions of Weibull and Fréchet are taken into account. Using these two distributions as marginal distributions in the copula a Bayesian inference was made in order to estimate the parameters of both distributions and also the association parameters appearing in the copula model. The pollutants taken into account are ozone, nitrogen dioxide, sulphur dioxide, carbon monoxide, and particulate matter with diameters smaller than 10 and 2.5 microns obtained from the Mexico City monitoring network. The estimation was performed by taking samples of the parameters generated through a Markov chain Monte Carlo algorithm implemented using the software OpenBugs. Once the algorithm is implemented it is applied to the pairs of pollutants where one of the coordinates of the pair is ozone and the other varies on the set of the remaining pollutants. Depending on the pollutant and the region where they were collected, different results were obtained. Hence, in some cases we have that the best model is that where we have a Fréchet distribution as the marginal distribution for the measurements of both pollutants and in others the most suitable model is the one assuming a Fréchet for ozone and a Weibull for the other pollutant. Results show that, in the present case, the estimated association parameter is a good representation to the correlation parameters between the pair of pollutants analyzed. Additionally, it is a straightforward task to obtain these correlation parameters from the corresponding association parameters.

Understanding of the Fate of Atmospheric Pollutants Using a Process Analysis Tool in a 3-D Regional Air Quality Model at a Fine Grid Scale

The process analysis is performed for August and December, 2002 using the process analysis tool embedded in the Community Multiscale Air Quality (CMAQ) modeling system at a fine horizontal grid resolution of 4-km over an area in the southeastern U.S. that is centered at North Carolina. The objectives are to qunatify the contributions of major atmospheric processes to the formation of major air pollutants and provide the insights into photochemistry that governs the fate of these pollutants at a fine grid scale. The results show that emissions provide a dominant source for gases including ammonia (NH3), nitric oxide (NO), nitrogen dioxide (NO2), and sulfur dioxide (SO2) and Particulate Matter (PM) species including fine PM (PM2.5) and its composition such as sulfate, elemental carbon, primary organic aerosol, and other inorganic fine PM in both months. While transport acts as a major sink for NH3, NO, and SO2 at most sites and PM2.5 and most of PM2.5 composition at urban sites, it provides a major source for nitric acid (HNO3) and ozone (O3) at most sites in both months, and secondary PM species in August and most PM species in December at rural and remote sites. Gas-phase chemistry serves as a source for NO2 and HNO3 but a sink for O3 at urban and suburban sites and for NO and SO2 at all sites. PM processes contribute to the formation of PM2.5 and nitrate () at the urban and suburban sites and secondary organic aerosol (SOA) at most sites in December and ammonium () in both months. They reduce formation at most sites in August and at rural and remote sites in December and the formation of PM2.5 and SOA at most sites in August. Dry deposition is an important sink for all these species in both months. The total odd oxygen (Ox) production and the total hydroxyl radical (OH) reacted are much higher at urban and suburban sites than at rural sites. Significant amounts of OH are consumed by biogenic volatile organic compounds (BVOCs) in the rural and remote areas and a combination of anthropogenic VOCs (AVOCs) and BVOCs in urban and subareas areas in August and mainly by AVOCs in December. The amount of NO2 produced by the reactions of hydroperoxy radical (HO2) is similar to that of organic peroxy radical (RO2) at all sites in August but higher than that by the reactions of RO2 inDecember. The production rate of HNO3 due to the reaction of OH with NO2 dominates in both months. The ratio of the production rates of hydrogen peroxide (H2O2) and HNO3 (PH2O2/PHNO3) is a more robust photochemical indicator than the ratios of their mixing ratios (H2O2/HNO3) and the afternoon mixing ratios of NOy in both months, and it is highly sensitive to the horizontal grid resolution in August. The use of PH2O2/PHNO3 simulated at 4-km indicates a VOC-limited O3 chemistry in urban and suburban areas in August that was not captured in previous model simulations at a coarser grid resolution.

Relationship of meteorological factors and air pollutants with medical care utilization for gastroesophageal reflux disease in urban area

BACKGROUND Gastroesophageal reflux disease(GERD)is a highly prevalent disease of the upper gastrointestinal tract,and it is associated with environmental and lifestyle habits.Due to an increasing interest in the environment,several groups are studying the effects of meteorological factors and air pollutants(MFAPs)on disease development.AIM To identify MFAPs effect on GERD-related medical utilization.METHODS Data on GERD-related medical utilization from 2002 to 2017 were obtained from the National Health Insurance Service of Korea,while those on MFAPs were obtained from eight metropolitan areas and merged.In total,20071900 instances of GERD-related medical utilizations were identified,and 200000 MFAPs were randomly selected from the eight metropolitan areas.Data were analyzed using a multivariable generalized additive Poisson regression model to control for time trends,seasonality,and day of the week.RESULTS Five MFAPs were selected for the prediction model.GERD-related medical utilization increased with the levels of particulate matter with a diameter≤2.5μm(PM2.5)and carbon monoxide(CO).S-shaped and inverted U-shaped changes were observed in average temperature and air pollutants,respectively.The time lag of each variable was significant around nine days after exposure.CONCLUSION Using five MFAPs,the final model significantly predicted GERD-related medical utilization.In particular,PM2.5 and CO were identified as risk or aggravating factors for GERD.