Air Quality Variables Research Articles

Assessment of Seasonal Variations in Air Quality from Lagos Metropolis and Suburbs Using Chemometric Models

Assessment of Seasonal Variations in Air Quality from Lagos Metropolis and Suburbs Using Chemometric Models

Fractal analysis of impact of PM2.5 on surface O3 sensitivity regime based on field observations

Properties of PM2.5 that can change aerosol chemistry and photolysis rates have great impacts on O3 sensitivity regime, further affecting the production rate of surface O3. However, responses of O3 sensitivity regime to changes in PM2.5 levels are difficult to be accurately determined, due to the complexity and nonlinearity of atmospheric chemistry. Here, based on long-term time series (2016–2020) of air quality variables in north and south Taiwan, fractal analysis along with Pearson correlation analysis are used to directly reveal the impacts of PM2.5 on O3 sensitivity regime in real atmosphere, by capturing the nonlinear dynamic relations among air pollutants. Great regional and seasonal difference in impacts of PM2.5 on O3 sensitivity regime may be ascribed to meteorological factors, PM2.5 components and levels of SO2, NO, NO2, etc. For north Taiwan, increased PM2.5 level can enhance the sensitivity of O3 formation to VOC in spring and summer, whereas the opposite effect can be observed in winter. But for south Taiwan, the influence of PM2.5 on O3 sensitivity regime is not statistically significant, excluding spring. Furthermore, feasibility and availability of fractal analysis is tested by simulations with Empirical Kinetics Modeling Approach (EKMA). The results demonstrate the capability of fractal analysis to identify the impacts of PM2.5 on O3 sensitivity regime in real atmosphere, which can provide suggestions for PM2.5-O3 coordinated control strategies in regions suffering combined air pollution.

Formation mechanisms and atmospheric implications of summertime nitrous acid (HONO) during clean, ozone pollution and double high-level PM2.5 and O3 pollution periods in Beijing

Nitrous acid (HONO) is a key precursor of the hydroxyl radicals (OH) and has a significant impact on air quality. Nowadays, the source of HONO is still controversial due to its complex formation mechanisms, which is widely explored in extensive field and laboratory studies. In this study, the pollution characteristics and source contribution of HONO under different air quality conditions in summer in Beijing were analyzed. The observation periods were classified as three typical periods: clean, ozone pollution, and double high pollution (co-occurrence of high PM2.5 and O3 concentrations). The average concentrations of observed HONO were 0.38 ± 0.35 ppb, 0.21 ± 0.18 ppb, 0.26 ± 0.20 ppb and 0.54 ± 0.45 ppb during the whole, clean, ozone and double high periods, respectively. The elevated HONO levels at night were attributed to vehicle emissions and the RH-dependent heterogeneous conversion of NO2 to HONO. The average emission ratio (HONO/NOx) was 0.85 % ± 0.38 %, and the mean value of calculated nocturnal NO2 to HONO conversion frequency was 0.0076 ± 0.0031 h−1. Based on daytime HONO budget analysis, the largest potential source of HONO was the homogeneous reaction of NO and OH (0.33 and 0.34 ppb h−1), followed by the unknown source (0.11 and 0.21 ppb h−1) during clean and ozone periods, while the unknown source (0.49 ppb h−1) played the predominant role during double high period. The unknown sources of HONO could be attributed to the photo-enhanced heterogeneous conversion of NO2 and the photolysis of particulate nitrate. Furthermore, the photolysis of ozone (0.17, 0.34 and 0.44 ppb h−1) was the major contributor to primary OH during three typical periods. HONO photolysis contributed considerable amounts of primary OH (0.32 ppb h−1) during double high period. These results are helpful to further understand the linkage between HONO and air quality variation.

Ambient Air Quality and Emergency Hospital Admissions in Singapore: A Time-Series Analysis.

Air pollution exposure may increase the demand for emergency healthcare services, particularly in South-East Asia, where the burden of air-pollution-related health impacts is high. This article aims to investigate the association between air quality and emergency hospital admissions in Singapore. Quasi-Poisson regression was applied with a distributed lag non-linear model (DLNM) to assess the short-term associations between air quality variations and all-cause, emergency admissions from a major hospital in Singapore, between 2009 and 2017. Higher concentrations of SO2, PM2.5, PM10, NO2, and CO were positively associated with an increased risk of (i) all-cause, (ii) cardiovascular-related, and (iii) respiratory-related emergency admissions over 7 days. O3 concentration increases were associated with a non-linear decrease in emergency admissions. Females experienced a higher risk of emergency admissions associated with PM2.5, PM10, and CO exposure, and a lower risk of admissions with NO2 exposure, compared to males. The older adults (≥65 years) experienced a higher risk of emergency admissions associated with SO2 and O3 exposure compared to the non-elderly group. We found significant positive associations between respiratory disease- and cardiovascular disease-related emergency hospital admissions and ambient SO2, PM2.5, PM10, NO2, and CO concentrations. Age and gender were identified as effect modifiers of all-cause admissions.

A machine learning approach to investigate the build-up of surface ozone in Mexico-City

Ground-level ozone is an important pollutant regarding air quality and climate. Mexico City frequently experiences severe ozone episodes due to a combination of strong ozone precursor emissions and its specific topographical environment which critically impacts meteorological conditions. High ozone levels during these episodes cause harmful effects to the public health and the environment. This necessitates ranking air quality and meteorological variables according to their contributions towards the build-up of ozone. In this study, three machine learning models are used to learn a prediction function with hourly data of eight predictors as input and hourly ground-level ozone mixing ratios as output. One-year hourly data of eight predictors collected in Mexico-City from March 2015 to February 2016 is employed to train and test the models. The best model, capturing ozone peak levels with 92% accuracy during 6–18 March 2016, is used to rank the predictors according to their importance in the build-up of ozone applying a shapley additive explanations approach based on the game theory shapley values. This 6–18 March 2016 period encompassed different meteorological and emission conditions and included a severe ozone smog episode from 12 to 17 March 2016. Such ranking of the air quality and meteorological variables is crucial for policy-making decisions regarding the prevention and mitigation of ozone detrimental effects during severe ozone episodes and provides insight into the functional dependency of ozone on its predictors. The proposed approach showcases Mexico City, but its principles can be applied for ozone episodes at any other location.

Understanding and revealing the intrinsic impacts of the COVID-19 lockdown on air quality and public health in North China using machine learning.

To avoid the spread of COVID-19, China implemented strict prevention and control measures, resulting in dramatic variations in urban and regional air quality. With the complex effect from long-term emission mitigation and meteorology variation, an accurate evaluation of the net effect from lockdown on air quality changes has not been fully quantified. Here, we combined machine learning algorithm and Theil–Sen regression technique to eliminate meteorological and long-term trends effects on air pollutant concentrations and precisely detect concentrations changes those ascribed to lockdown measures in North China. Our results showed that, compared to the same period in 2015–2019, the adverse meteorology during the lockdown period (January 25th to March 15th) in early 2020 increased PM2.5 concentration in North China by 9.8 %, while the reduction of anthropogenic emissions led to a 32.2 % drop. Stagnant meteorological conditions have a more significant impact on the ground-level air quality in the Beijing-Tianjin-Hebei Region than that in Shanxi and Shandong provinces. After further striping out the effect of long-term emission reduction trend, the lockdown-derived NO2, PM2.5, and O3 shown variety change trend, and at −30.8 %, −27.6 %, and +10.0 %, respectively. Air pollutant changes during the lockdown could be overestimated up to 2-fold without accounting for the influences of meteorology and long-term trends. Further, with pollution reduction during the lockdown period, it would avoid 15,807 premature deaths in 40 cities. If with no deteriorate meteorological condition, the total avoided premature should increase by 1146.

The influence of air quality and meteorological variations on influenza A and B virus infections in a paediatric population in Singapore

IntroductionInfluenza is an important cause of paediatric illness across the globe. However, information about the relationships between air pollution, meteorological variability and paediatric influenza A and B infections in tropical settings is limited. MethodsWe analysed all daily reports of influenza A and B infections in children <5 years old obtained from the largest specialist women and children's hospital in Singapore. In separate negative binomial regression models, we assessed the dependence of paediatric influenza A and B infections on air quality and meteorological variability, using multivariable fractional polynomial modelling and adjusting for time-varying confounders. ResultsApproximately 80% of 7329 laboratory-confirmed reports were caused by influenza A. We observed positive associations between sulphur dioxide (SO2) exposure and the subsequent risk of infection with both influenza types. We observed evidence of a harvesting effect of SO2 on Influenza A but not Influenza B. Ambient temperature was associated with a decline in influenza A reports (Relative Risk at lag 5 [RRlag5]: 0.949, 95% CI: 0.916–0.983). Rainfall was positively associated with a subsequent increase in influenza A reports (RRlag3: 1.044, 95% CI: 1.017–1.071). Nitrogen dioxide (NO2) concentration was positively associated with influenza B reports (RRlag5: 1.015, 95% CI: 1.005–1.025). There was a non-linear association between CO and influenza B reports. Absolute humidity increased the ensuing risk of influenza B (RRlag5: 4.799, 95% CI: 2.277–10.118). Influenza A and B infections displayed dissimilar but predictable within-year seasonal patterns. ConclusionsWe observed different independent associations between air quality and meteorological variability with paediatric influenza A and B infections. Anticipated seasonal infection peaks and variations in air quality and meteorological parameters can inform the timing of community measures aimed at reducing influenza infection risk.

The relationship among air pollution, meteorological factors and COVID-19 in the Brussels Capital Region.

In great metropoles, there is a need for a better understanding of the spread of COVID-19 in an outdoor context with environmental parameters. Many studies on this topic have been carried out worldwide. However, there is conflicting evidence regarding the influence of environmental variables on the transmission, hospitalizations and deaths from COVID-19, even though there are plausible scientific explanations that support this, especially air quality and meteorological factors. Different urban contexts, methodological approaches and even the limitations of ecological studies are some possible explanations for this issue. That is why methodological experimentations in different regions of the world are important so that scientific knowledge can advance in this aspect. This research analyses the relationship between air pollution, meteorological factors and COVID-19 in the Brussels Capital Region. We use a data mining approach that is capable of extracting patterns in large databases with diverse taxonomies. Data on air pollution, meteorological, and epidemiological variables were processed in time series for the multivariate analysis and the classification based on association. The environmental variables associated with COVID-19-related deaths, cases and hospitalization were PM2.5, O3, NO2, black carbon, radiation, air pressure, wind speed, dew point, temperature and precipitation. These environmental variables combined with epidemiological factors were able to predict intervals of hospitalization, cases and deaths from COVID-19. These findings confirm the influence of meteorological and air quality variables in the Brussels region on deaths and cases of COVID-19 and can guide public policies and provide useful insights for high-level governmental decision-making concerning COVID-19. However, it is necessary to consider intrinsic elements of this study that may have influenced our results, such as the use of air quality aggregated data, ecological fallacy, focus on acute effects in the time-series study, the underreporting of COVID-19, and the lack of behavioral factors.

Kernel PLS with AdaBoost ensemble learning for particulate matters forecasting in subway environment

Excessive exposure to hazardous air pollutants remains the leading cause of serious cardio-respiratory disease among commuters. To monitor and improve the air quality in time, soft-sensing techniques in metro systems need to be introduced. Adaptive boosting (AdaBoost), as an ensemble learning method, has greater flexibility and adaptability. Compared with a single conventional model, it has a stronger capability to explain the complex features contained in air quality data. In this study, an improved scheme employs AdaBoost to predict key air quality variables, such as PM2.5, in subway environment. To eliminate the adverse effects of noise interference and the collinearity problem among data variables, kernel latent variables are embedded into the original AdaBoost. The proposed method combines kernel partial least squares with AdaBoost (KPLS-AdaBoost). Root mean square error (RMSE) was used to evaluate the prediction performance of KPLS-AdaBoost. The results demonstrate that KPLS-AdaBoost could achieve better modeling performance, providing a new way to realize real-time monitoring of hazardous air pollutants. More precisely, in terms of PM2.5 in the hall, the RMSE value was optimized by approximately 14.44–31.86% compared with that of AdaBoost, random forest, artificial neural network, PLS, KPLS, and PLS-AdaBoost. For PM2.5 at the platform, RMSE value can be decreased by 13.57–38.51%.

Influence of environmental variables on functional capacity in patients with lung disease

Air pollution is related to negative impacts mainly on people with Chronic Respiratory Diseases (CRD), reflecting on respiratory health, reduced physical performance and so on. The objective was to investigate the influence of meteorological and air quality variables on the functional capacity of patients with CRD. This was a descriptive, retrospective and cross-sectional study of information collection in a database from 2016 to 2019. For the analysis of environmental variables, three different databases were used, one from the automatic air quality monitoring station in Novo Hamburgo, and the weather stations of Novo Hamburgo and Campo Bom (RS). The monitored data were: NO2, CO, O3, PM10, PM2,5, average temperature, minimum temperature, maximum temperature, precipitation, average wind speed and relative humidity. The sample consisted of 85 individuals from the Pulmonary Rehabilitation Program (PRP), where the variables spirometry and six-minute walk test (6MWT) were collected. It was observed that the higher the concentration of PM10 and the higher the minimum temperature, the lower the spirometric results. Furthermore, the greater the concentration of PM2.5, the shorter the distance that the individual travels. A relationship was found between environmental data and functional tests, where individuals with CRD are more sensitive to high levels of air pollutants, as well as to lower temperatures.

Assessment of Seasonal Variations of Air Quality and AQI Status: Evidence from Chittagong, Bangladesh

With the rate of fast urbanization, the devasting effects of air pollution are spreading across the globe. Due to its connection to people’s health, air quality should be given more importance than other environmental measures. Air pollution is considered a cause of many human diseases. Therefore, this study intends to investigate seasonal variation of air quality and “Air Quality Index (AQI)” in Chittagong city due to its volume, large population density, and importance as a commercial capital city of Bangladesh. Air pollution data on PM10, PM2.5, NO2 , NOx, SOx, CO, and O3 levels have been collected from TV station, Khulshi Continuous Air Monitoring Station (CAMS). Component-specific analyzers have been used to continuously measure trace gases where O3 is observed with a UV photometric analyzer. This study detect the highest peak (PM2.5= 93.5 µg/m3, PM10= 210 µg/m3) in January and the lowest concentrations (PM2.5= 14.6 µg/m3 and PM10= 26.9 µg/m3 ) during July and August. The highest average concentration has been recorded as the value of SO2= 12.8 ppb (monsoon season), NO2= 64.9 ppb (pre-monsoon), CO= 1.2 ppm (monsoon) and lowest SO2= 3.2 ppb (winter season), NO2= 24.4 ppb (monsoon), CO= 0.6 ppm (pre-monsoon) respectively. The AQI values (223.6), (109.5), (194.5), and (317.3) indicate that the air quality during the pre-monsoon, monsoon, post-monsoon, and winter season is very unhealthy, cautious, unhealthy, and extremely unhealthy, respectively.

Implementing a novel deep learning technique for rainfall forecasting via climatic variables: An approach via hierarchical clustering analysis

Variations in rainfall negatively affect crop productivity and impose severe climatic conditions in developing regions. Studies that focus on climatic variations such as variability in rainfall and temperature are vital, particularly in predominant rainfed areas. Forecasting rainfall is very essential in the agriculture sector due to the dependence of many people, while it is very complex to accurately predict rainfall due to its dynamic nature. This study aims to present a deep forecasting model based on optimized (Gated Recurrent Unit) GRU neural network to predict rainfall in Pakistan based on the 30 years of climate data from 1991 to 2020. The climatic variables were first extracted and then fine-tuned by eliminating outliers and extreme values from the data set for precise forecasting. Data normalization strategies were further utilized to adjust numeric values into a standard scale without distorting divergences or losing useful information. The proposed model achieved high prediction accuracy by maintaining minimal Normalized Mean Absolute Error (NMAE) and Normalized Root Mean Squared Error (NRMSE) compared to state-of-the-art rainfall forecasting models. Climatic variables used in the forecasting were evaluated in terms of correlation and regression analysis. The correlation results showed that temperature has a negative association and air quality variables have a positive association with rainfall in each quarter of the year. The second and third quarters of the year showed a high association with rainfall, whereas the air quality variables showed a lesser or no association with rainfall during the first and second quarters of the year. The results further showed a strong association of climatic variables with rainfall for all months of the year. The minimal loss achieved by the proposed model also demonstrated the feasibility of selected variables in precise forecasting of rainfall regardless of volatile climatic conditions.

Assessment of urban air quality from Twitter communication using self-attention network and a multilayer classification model.

Social media platforms are one of the prominent new-age methods used by public for spreading awareness or drawing attention on an issue or concern. This study demonstrates how the twitter responses of public can be used for qualitative monitoring of air pollution in an urban area. Tweets discussing about air quality in Delhi, India, were extracted during 2019–2020 using a machine learning technique based on self-attention network. These tweets were cleaned, sorted, and classified into 3-class quality viz. poor air quality, good air quality, and noise or neutral tweets. The present study used a multilayer classification model with first layer as an embedding layer and second layer as bi-directional long-short term memory (BiLSTM) layer. A method was then devised for estimating PM2.5 concentration from the tweets using ‘spaCy’ similarity analysis of classified tweets and data extracted from Continuous Ambient Air Quality Monitoring Stations (CAAQMS) in Delhi for the study period. The accuracy of this estimation was found to be high (80–99%) for extreme air quality conditions (extremely good or severe) and lower during moderate variations in air quality. Application of this methodology depended on perceivable changes in air quality, twitter engagement, and environmental consciousness among public.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11356-022-22836-w.

Partnership to Develop and Deliver Curriculum Supporting Student-led Air Quality Research in Rural Washington State.

This article describes the process and educational materials developed and implemented for high school students through a partnership between an urban public university and a rural, non-profit university. This specific partnership was novel but originated within a long-standing community-academic partnership. This project took place in a rural community impacted by air pollution and a higher asthma hospitalization rate compared with the rest of the state. The objectives of this article are to describe the development and implementation of a high school program where students conducted their own research on local air quality using low-cost monitors with the guidance of undergraduate student mentors. University faculty, researchers, and students collaborated to develop an air quality curriculum relevant to local issues. This curriculum was delivered to high school students through an existing after school program, and guided students in conducting their own research on community air pollution. The students used university-provided low-cost monitors for their research, and presented their research to community members. Student learning was supported through hands-on activities and conducting research projects. Student projects examined air quality variation indoors within their school, outdoors in their community, and at home. This curriculum can be adapted for use with students in many different communities. It will likely be most successful and engaging if adapted to local air pollution sources and issues, and implemented through an existing programmatic structure with a high mentor to student ratio.

Spatial-temporal characterization of air pollutants using a hybrid deep learning/Kriging model incorporated with a weather normalization technique

A scarce distribution of the PM2.5 chemical compositions monitors reduces the applicability of scientific information for policymakers to assess the effectiveness of air pollution control strategies. There is an urgent need for a spatial-temporal prediction model for characterizing PM2.5 chemical compositions to assess exposure risks and develop effective air pollutants reduction strategies. In this study, the spatial-temporal variations of NO3− and SO42− were characterized using a hybrid multi-step ahead neural network (MSA-NN)/Kriging model in the urban areas with limited PM2.5 constituents monitoring stations. A meteorological normalization technique was further applied to develop a de-weather model to investigate temporal variations of air pollutants during the level 3 COVID-19 alert in central Taiwan. The MSA-NN algorithm could predict 94% and 91% of NO3− and SO42−, respectively, at the t+1-time horizon predictions. Based on the predicted results using the present de-weather model, the reduction in primary emissions attributed to the impact of COVID-19 during the level 3 alert was found to dominate the temporal air pollutant concentrations in central Taiwan. The present model could provide applicable and accurate high resolution of spatial-temporal NO3− and SO42− datasets in an area with limited PM2.5 chemical composition measurement. The present model could also be potentially applied to facilitate hotspot identification and human exposure assessment. The present Artificial Neural Network-based de-weather model is applicable to predict meteorological normalized time series air pollutant concentrations, which could be used to verify the effects of the meteorological parameters and primary emissions on the variations in air quality during the implementation of a specific air quality control strategy or changes in anthropogenic activities.

PM10 variation, composition, and source analysis in Tuscany (Italy) following the COVID-19 lockdown restrictions

Abstract. To control the spread of COVID-19, exceptional restrictive measures were taken in March 2020 that imposed a radical change on the lifestyle of millions of citizens around the world, albeit for a short period. The national lockdown, which lasted from 10 March to 18 May 2020 in Italy, was a unique opportunity to observe the variation in air quality in urban environments under conditions comprising almost total traffic restriction and a strong reduction in work activities. In this paper, the data from 17 urban monitoring sites in Tuscany are presented, and the PM and NO2 concentrations in the 2 months before the start of the lockdown and the 2 months after lockdown are compared with the corresponding months of the previous 3 years. The results show that the total loads of PM2.5 and PM10 (particulate matter with an aerodynamic diameter smaller than 2.5 and 10 µm, respectively) decreased, but they did not exhibit significant changes compared to previous years, whereas NO2 underwent a drastic reduction. For three of these sites, the chemical composition of the collected samples was measured using thermal–optical techniques, ion chromatography, and particle-induced X-ray emission analysis, and the application of multivariate positive matrix factorization analysis also allowed for PM10 source identification and apportionment. Using these analyses, it was possible to explain the low sensitivity of PM10 to the lockdown effects as being due to different, sometimes inverse, behaviors of the different sources that contribute to PM. The results clearly indicate a decline in pollution levels related to urban traffic and an increase in the concentration of sulfate for all sites during the lockdown period.

Assessment of variation in air quality over Delhi and neighbouring cities of Noida and Greater Noida

Assessment of variation in air quality over Delhi and neighbouring cities of Noida and Greater Noida

Air quality changes in China 2013–2020: Effectiveness of clean coal technology policies

Clean and efficient use of coal in China entered the implementation stage in 2015, and a series of policies were established to promote the sustainable development of clean coal technologies (CCTs). However, the long-term effects of the clean coal technology policies (CCTPs) have not been evaluated, and the outbreak of coronavirus disease 2019 (COVID-19) would bring great uncertainty regarding the effectiveness evaluation. Employing deep learning approaches and spatial autocorrelation analysis, the present work intended to explore the air quality variation before and after the implementation of CCTPs, and investigate the association between air quality and socioeconomic factors to explore the internal mechanism of air quality improvement. Results showed that after implementing CCTPs, the air quality index (AQI) had an average reduction of 18.82%, and the identical drop in air pollution in 2018 implied a 2-year time lag of CCTPs. Additionally, emission reduction and deindustrialization were explored as two promising ways to improve air quality while promoting energy transformation. The findings indicated that the Chinese government ought to pay more attention to long-term plans; industrial restructuring and environmental protection should be insisted upon to realize sustainable energy development. By providing a perspective on air quality improvement after the policies are implemented, this study can lead practitioners and academia to a comprehensive and objective view of clean coal policies.

Factors determining the seasonal variation of ozone air quality in South Korea: Regional background versus domestic emission contributions

South Korea has experienced a rapid increase in ozone concentrations in surface air together with China for decades. Here we use a 3-D global chemical transport model, GEOS-Chem nested over East Asia (110 E - 140 E, 20 N–50 N) at 0.25° × 0.3125° resolution, to examine locally controllable (domestic anthropogenic) versus uncontrollable (background) contributions to ozone air quality at the national scale for 2016. We conducted model simulations for representative months of each season: January, April, July, and October for winter, spring, summer, and fall and performed extensive model evaluation by comparing simulated ozone with observations from satellite and surface networks. The model appears to reproduce observed spatial and temporal ozone variations, showing correlation coefficients (0.40–0.87) against each observation dataset. Seasonal mean ozone concentrations in the model are the highest in spring (39.3 ± 10.3 ppb), followed by summer (38.3 ± 14.4 ppb), fall (31.2 ± 9.8 ppb), and winter (24.5 ± 7.9 ppb), which is consistent with that of surface observations. Background ozone concentrations obtained from a sensitivity model simulation with no domestic anthropogenic emissions show a different seasonal variation in South Korea, showing the highest value in spring (46.9 ± 3.4 ppb) followed by fall (38.2 ± 3.7 ppb), winter (33.0 ± 1.9 ppb), and summer (32.1 ± 6.7 ppb). Except for summer, when the photochemical formation is dominant, the background ozone concentrations are higher than the seasonal ozone concentrations in the model, indicating that the domestic anthropogenic emissions play a role as ozone loss via NOx titration throughout the year. Ozone air quality in South Korea is determined mainly by year-round regional background contributions (peak in spring) with summertime domestic ozone formation by increased biogenic VOCs emissions with persistent NOx emissions throughout the year. The domestic NOx emissions reduce MDA8 ozone around large cities (Seoul and Busan) and hardly increase MDA8 in other regions in spring, but it increases MDA8 across the country in summer. Therefore, NOx reduction can be effective in control of MDA8 ozone in summer, but it can have rather countereffect in spring.

Seasonal variation of atmospheric total gaseous mercury and urban air quality in South India

&lt;p&gt;This study analyses seasonal and regular variations in ambient atmospheric concentrations of total gaseous mercury (TGM), ancillary air pollutant concentrations, and their relationship to weather conditions of a megacity environment in India. Further, to correlate with TGM concentrations at different megacities all around the urbanized environments in the world. The average concentration for TGM was 4.66 ± 8.35 ng/m3 and with an absorptions range of 0.41∼540 ng/m3 throughout the study period. The maximum concentration of total gaseous mercury was observed at 5.64 ng/m3 during the winter, while the minimum concentration was 3.91 ng/m3 during the southwest monsoon. The multivariate statistical analysis indicates the TGM values interacted positively with SR, WD, BP, CO, PM2.5, NO2, NOx and negatively with SO2, relative humidity, wind speed, and temperature. Meteorological parameters have an essential influence on the transport and distribution of pollutants in the monitoring site due to its location, local sources, and anthropogenic activities. Wind across India belongs to the southern hemisphere in summer and to the northern hemisphere in winter due to the movement of the intertropical convergence zone. Limited studies of atmospheric mercury concentrations have been conducted in India's city environments; however, as far as our results are concerned, they are the essentials, which exposed about environmental aggregate vaporous mercury detected in a required industrialized and coastal metropolitan zone in India.&lt;/p&gt;&#x0D;