Changes In Pollutant Concentrations Research Articles

Stochastic dynamics of an SIR model for respiratory diseases coupled air pollutant concentration changes

Industrial development has made air pollution increasingly severe, and many respiratory diseases are closely related to air quality in terms of infection and transmission. In this work, we used the classic stochastic susceptible–infectious–recovered (SIR) model to reflect the spread of respiratory disease, coupled with the diffusion process of air pollutants to the infectious disease model, and we investigated the impact of various environmental noises on the process of disease transmission and air pollutant diffusion. The value of this study lies in two aspects. Mathematically, we define threshold R1s\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$\\mathcal{R}_{1}^{s}$\\end{document} for extinction and threshold R2s\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$\\mathcal{R}_{2}^{s}$\\end{document} for persistence of the disease in the stochastic model (R2s<R1s\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$\\mathcal{R}_{2}^{s}<\\mathcal{R}_{1}^{s}$\\end{document}) when the parameters are constant, and we show that (i) when R1s\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$\\mathcal{R}_{1}^{s}$\\end{document} is less than 1, the disease will go to stochastic extinction; (ii) when R2s\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$\\mathcal{R}_{2}^{s}$\\end{document} is larger than 1, the disease will persist almost surely and the model has a unique ergodic stationary distribution; (iii) when R1s\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$\\mathcal{R}_{1}^{s}$\\end{document} is larger than 1 and R2s\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$\\mathcal{R}_{2}^{s}$\\end{document} is less than 1, the extinction of the disease has randomness, which is demonstrated through numerical experiments. In addition, we derive the exact expression of the probability density function of the stationary distribution by solving the corresponding Fokker–Planck equation under the condition of disease persistence and analyze the effects of random noises on stationary distribution characteristics and the disease extinction. Epidemiologically, the change of the concentration of air pollutants affects the conditions for disease extinction and persistence. The increase in the inflow of pollutants and the increase in the clearance rate have negative and positive impacts on the spread of diseases, respectively. We found that an increase in random noise intensity will increase the variance, reduce the kurtosis of distribution, which is not conducive to predicting and controlling the development status of the disease; however, large random noise intensity can also increase the probability of disease extinction and accelerates disease extinction. We further investigate the dynamic of the stochastic model, assuming that the inflow rate switches between two levels by numerical experiments. The results show that the random noise has a significant impact on disease extinction. The data fitting of the switching model shows that the model can effectively depict the relationship and changes in trends between air pollution and diseases.

Elucidating the Effects of COVID-19 Lockdowns in the UK on the O3-NOx-VOC Relationship

The unprecedented reductions in anthropogenic emissions over the COVID-19 lockdowns were utilised to investigate the response of ozone (O3) concentrations to changes in its precursors across various UK sites. Ozone, volatile organic compounds (VOCs) and NOx (NO+NO2) data were obtained for a 3-year period encompassing the pandemic period (January 2019–December 2021), as well as a pre-pandemic year (2017), to better understand the contribution of precursor emissions to O3 fluctuations. Compared with pre-lockdown levels, NO and NO2 declined by up to 63% and 42%, respectively, over the lockdown periods, with the most significant changes in pollutant concentrations recorded across the urban traffic sites. O3 levels correspondingly increased by up to 30%, consistent with decreases in the [NO]/[NO2] ratio for O3 concentration response. Analysis of the response of O3 concentrations to the NOx reductions suggested that urban traffic, suburban background and suburban industrial sites operate under VOC-limited regimes, while urban background, urban industrial and rural background sites are NOx-limited. This was in agreement with the [VOC]/[NOx] ratios determined for the London Marylebone Road (LMR; urban traffic) site and the Chilbolton Observatory (CO; rural background) site, which produced values below and above 8, respectively. Conversely, [VOC]/[NOx] ratios for the London Eltham (LE; suburban background) site indicated NOx-sensitivity, which may suggest the [VOC]/[NOx] ratio for O3 concentration response may have had a slight NOx-sensitive bias. Furthermore, O3 concentration response with [NO]/[NO2] and [VOC]/[NOx] were also investigated to determine their relevance and accuracy in identifying O3-NOx-VOC relationships across UK sites. While the results obtained via utilisation of these metrics would suggest a shift in photochemical regime, it is likely that variation in O3 during this period was primarily driven by shifts in oxidant (OX; NO2 + O3) equilibrium as a result of decreasing NO2, with increased O3 transported from Europe likely having some influence.

Reducing air pollution does not necessarily reduce related adults' mortality burden: Variations in 177 countries with different economic levels

Prolonged exposure to PM2.5 is associated with increased mortality. However, reducing air pollution concentrations does not necessarily reduce the related burden of deaths. Here, we aim to estimate the variations in PM2.5-related mortality due to contributions from key factors – PM2.5 concentration, population exposure, and healthcare levels – for 177 countries from 2000 to 2018 at the 1-km grid scale according to the Global Mortality Exposure Model (GEMM) model. We find that global reductions in PM2.5-related deaths mainly come from high and upper-middle income countries, where lowered air pollutant concentration and better healthcare can offset mortality burdens caused by increasing exposed populations. Changes in population exposure to PM2.5 contribute the most (54 %) to change in global related deaths over the examined period, followed by changes in healthcare (−42 %) and pollution concentrations (4 %). The impacts vary across countries and regions within them due to other drivers, which are significantly influenced by development status. Policies aiming at reducing PM2.5 associated health risks need to account for country-specific balances of these key socioeconomic drivers.

Societal Co-benefits of Zero-Emission Vehicles in the Freight Industry.

This study was set in the Greater Toronto and Hamilton Area (GTHA), where commercial vehicle movements were assigned across the road network. Implications for greenhouse gas (GHG) emissions, air quality, and health were examined through an environmental justice lens. Electrification of light-, medium-, and heavy-duty trucks was assessed to identify scenarios associated with the highest benefits for the most disadvantaged communities. Using spatially and temporally resolved commercial vehicle movements and a chemical transport model, changes in air pollutant concentrations under electric truck scenarios were estimated at 1-km2 resolution. Heavy-duty truck electrification reduces ambient black carbon and nitrogen dioxide on average by 10 and 14%, respectively, and GHG emissions by 10.5%. It achieves the highest reduction in premature mortality attributable to fine particulate matter chronic exposure (around 200 cases per year) compared with light- and medium-duty electrification (less than 150 cases each). The burden of all traffic in the GTHA was estimated to be around 600 cases per year. The benefits of electrification accrue primarily in neighborhoods with a high social disadvantage, measured by the Ontario Marginalization Indices, narrowing the disparity of exposure to traffic-related air pollution. Benefits related to heavy-duty truck electrification reflect the adverse impacts of diesel-fueled freight and highlight the co-benefits achieved by electrifying this sector.

An observational study on the impact of sea-land breeze and low-level jet on air pollutant transport in the Bohai Bay

Nocturnal pollutant concentrations in Bohai Bay often exhibit significant uneven spatial distribution, even under weak atmospheric circulation and stable boundary layer conditions, posing a serious challenge for air pollution forecasting. This study analyzed the causes of inverse changes in nocturnal pollutant concentrations between inland and coastal areas based on a month-long observation campaign on the meteorology and air pollutants in Bohai Bay. The results indicated that the primary cause was the combined influence of sea-land breeze (SLB) and low-level jet (LLJ) under a weak synoptic pattern. Sea breezes (SB) can transport pollutants from the coast to inland areas. The occurrence of land breezes (LB) and southwest-west LLJ at night can lead to the transport of pollutants inland to the coast. The impacts of LLJ on pollutant transport can be observed in three ways. First, the LLJ enhanced the horizontal ventilation in the boundary layer. The horizontal wind speed in the stable boundary layer increased by 2–6 m/s. Second, the LLJ weakened the stability of the stable boundary layer. A thin layer of instability existed between the surface-based and elevated inversion layers. The height of the inversion layer top increased by approximately 200 m, and the strength of the inversion weakened by at least 1 °C. Finally, the LLJ enhanced atmospheric vertical diffusion. The increased turbulent kinetic energy and upward momentum flux in the lower layer of the boundary layer, as well as the pronounced vertical upward motion between the top of the boundary layer and the altitude of the LLJ, contributed to the vertical dispersion of ground pollutants. Pollutants diffusing to the altitude of the LLJ can be transported horizontally to the coastal areas along the southwest-west LLJ.

Temporal Evolution of Vehicle Exhaust Plumes in a Congested Street Canyon Environment

Air pollutants from traffic make an important contribution to human exposure, with pedestrians likely to experience rapid fluctuation and high concentrations on the pavements of busy streets. This monitoring campaign was on Hennessy Road in Hong Kong, a densely populated city with deep canyons, crowded footpaths and low wind speeds. Kerbside NOx concentrations were measured using electrochemical sensors with baseline correction and subsequently deconvoluted to determine concentrations at 1-s resolution to study the dispersion of exhaust gases within the first few metres of their on-road source. The pulses of NOx from passing vehicles were treated as segments of a Gaussian plume originating at the tailpipe. The concentration profiles in segments were fit to a simple analytical equation assuming a continuous line source with R2 &gt; 0.92. Least squares fitting parameters could be attributed to vehicle speed and source strength, dispersion, and sensor position. The width of the plume was proportional to the inverse of vehicle speed. The source strength of NOx from passing vehicles could be interpreted in terms of individual emissions, with a median value of approximately 0.18 g/s, but this was sensitive to vehicle speed and exhaust pipe position. The current study improves understanding of rapid changes in pollutant concentration in the kerbside environment and suggests opportunities to establish the contribution from traffic flow to pedestrian exposure in a dynamic heavily occupied urban microenvironment.

Investigation of river water pollution using Muskingum method and particle swarm optimization (PSO) algorithm

River water pollution is one of the most important environmental issues. Advection–dispersion equations are used to study the temporal changes in pollutant concentration along the study river reach. The use of advection–dispersion equations in investigating how the concentration of pollution is transformed requires a lot of data including river cross-section characteristics, dispersion coefficient, and upstream and downstream boundary conditions, etc. therefore, the corresponding calculations are very costly, difficult and time-consuming. In the present study, instead of using the mentioned equations, the linear Muskingum method (used in previous studies for flood routing) and the particle swarm optimization (PSO) algorithm was used for the first time to calculate the temporal changes in pollution concentration at different stream locations. The presented solution in the presented study is very accurate and only requires the temporal changes in concentration in the upstream and downstream of the study river reach and for this reason, it is very low-cost and easy to use and requires less time to collect data and perform calculations. In the proposed method, the parameters (X, K, ∆t) of the linear Muskingum method were optimized using the PSO algorithm, and by dividing the temporal changes in the input concentration into three areas of the beginning (the input concentration is greater than the output concentration), the peak (the maximum input and output concentrations) and the end (the output concentration is greater than the input concentration) areas, the accuracy of the calculations increased. The mentioned method was studied for different lengths (first case of x = 50 m (up) and x = 75 m (down), second case of x = 50 m (up) and x = 100 m (down), third case of x = 75 m (up) and x = 100 m (down)) and the mean relative error (MRE) of the total, peak area and the relative error of the maximum concentration using constant parameters for the first case were calculated as 7.08, 1.02, and 2.34 percent, for the second case as 7.41, 11.06 and 6.69 percent, and for the third case as 6.75, 3.59 and 5.42 percent, respectively. If three parameters of (X, K, ∆t) are used, the mentioned values improved by 31.3, 63.7 and 65.5 percent, respectively compared to the case of using constant parameters.

Preliminary study on the enhanced bioremediation of PAH-contaminated soil in Beijing and assessment of remediation effects based on toxicity tests.

In this study, we focused on soil contaminated by polycyclic aromatic hydrocarbons (PAHs) at typical coking-polluted sites in Beijing, conducted research on enhanced PAH bioremediation and methods to evaluate remediation effects based on toxicity testing, and examined changes in pollutant concentrations during ozone preoxidation coupled with biodegradation in test soil samples. The toxicity of mixed PAHs in soil was directly evaluated using the Ames test, and the correlation between mixed PAH mutagenicity and benzo(a)pyrene (BaP) toxicity was investigated in an effort to establish a carcinogenic risk assessment model based on biological toxicity tests to evaluate remediation effects on PAH-contaminated soil. This study provides a theoretical and methodological foundation for evaluating the effect of bioremediation on PAH-contaminated soil at industrially contaminated sites. The results revealed that the removal rate of PAHs after 5min of O3 preoxidation and 4weeks of soil reaction with saponin surfactants and medium was 83.22%. The soil PAH extract obtained after remediation had a positive effect on the TA98 strain at a dose of 2000μg·dish-1, and the carcinogenic risk based on the Ames toxicity test was 8.98 times greater than that calculated by conventional carcinogenic PAH toxicity parameters. The total carcinogenic risk of the remediated soil samples was approximately one order of magnitude less than that of the original soil samples.

Causal effect of aviation on air pollution: An instrumental variable from faraway COVID-19 restrictions in China

The causal impacts of aviation on local air pollution are poorly understood. Leveraging variation in aviation frequency caused by COVID-19 travel restrictions that occurred hundreds of miles away between 2020 and 2022, this study identifies the short-run effect of aviation on air pollution in Hangzhou, a Chinese megacity. The results demonstrate that a one standard deviation change in aviation is associated with 12% to 21.82% changes in ambient pollution concentrations, with even more substantial pollution effects on downwind days and flights departing from Hangzhou, respectively. These estimates also remain robust to alternative specifications, satisfy external validity beyond Hangzhou and the epidemic period, and exclude pollution spillover effects. We further quantify the welfare losses from aviation pollution and find that people are willing to pay 1.76 US dollars a day in per capita household income for reducing pollution caused by each standard deviation increase in flights (i.e., 134 flights). Further analysis reveals higher economic losses resulting from pollutants at international airports. Our results underscore the need to regulate airborne contaminants from aviation in China urgently.

Combined effect of changes in NO2, O3, PM2.5, SO2 and CO concentrations on small airway dysfunction.

When multiple complex air pollutants are combined in real-world settings, the reliability of estimating the effect of a single pollutant is questionable. This study aimed to investigate the combined effects of changes in air pollutants on small airway dysfunction (SAD). We analysed Korea National Health and Nutrition Examination Survey (KNHANES) V-VIII database from 2010 to 2018 to elucidate the associations between annual changes in air pollutants over a previous 5-year period and small airway function. We estimated the annual concentrations of five air pollutants: NO2, O3, PM2.5, SO2 and CO. Forced expiratory flow between 25% and 75% of vital capacity (FEF25%-75%) <65% was defined as SAD. Using the quantile generalized-Computation (g-Computation) model, the combined effect of the annual changes in different air pollutants was estimated. A total of 29,115 individuals were included. We found significant associations between SAD and the quartiles of annual changes in NO2 (OR = 1.10, 95% CI = 1.08-1.12), O3 (OR = 1.03, 95% CI = 1.00-1.05), PM2.5 (OR = 1.03, 95% CI = 1.00-1.05), SO2 (OR = 1.04, 95% CI = 1.02-1.08) and CO (OR = 1.16, 95% CI = 1.12-1.19). The combined effect of the air pollutant changes was significantly associated with SAD independent of smoking (OR = 1.31, 95% CI = 1.26-1.35, p-value <0.001), and this trend was consistently observed across the entire study population and various subgroup populations. As the estimated risk of SAD, determined by individual-specific combined effect models, increased and the log odds for SAD increased linearly. The combined effect of annual changes in multiple air pollutant concentrations were associated with an increased risk of SAD.

Study of summer microclimate and PM2.5 concentration in campus plant communities

Understanding the influencing effect of meteorological factors and air pollutants in the campus plot and the relationship between them is an important topic in the planning and design of campus green space. The changes of pollutant concentrations and meteorological factors in campus green space have certain patterns and specific influencing factors. In this study, we selected four sample plots in Nanjing Forestry University as the research objects, and collected various environmental parameters of the four plots on July 25, 2022. The results showed that the main influences of meteorological factors are the type of the underlying surface of the site, the degree of plant canopy density and the shade coverage area of the building. These factors mainly have a great influence on the value of temperature and humidity. The comprehensive influencing factors can be concluded that the cooling and humidifying effect of the site is ranked as follows: forest > lawn > asphalt road > concrete Square. The main influencing factors of pollutants are: illumination, wind speed, temperature and relative humidity. Among them, illumination and temperature have a negative correlation with PM2.5, wind speed and relative humidity have a positive correlation with PM2.5. Our research shows that the adjustment of campus green space factors can reduce the concentration of pollutants by changing the meteorological factors.

Reassessing the role of urban green space in air pollution control

The assumption that vegetation improves air quality is prevalent in scientific, popular, and political discourse. However, experimental and modeling studies show the effect of green space on air pollutant concentrations in urban settings is highly variable and context specific. We revisited the link between vegetation and air quality using satellite-derived changes of urban green space and air pollutant concentrations from 2,615 established monitoring stations over Europe and the United States. Between 2010 and 2019, stations recorded declines in ambient NO2, (particulate matter) PM10, and PM2.5 (average of -3.14% y-1), but not O3 (+0.5% y-1), pointing to the general success of recent policy interventions to restrict anthropogenic emissions. The effect size of total green space on air pollution was weak and highly variable, particularly at the street scale (15 to 60 m radius) where vegetation can restrict ventilation. However, when isolating changes in tree cover, we found a negative association with air pollution at borough to city scales (120 to 16,000 m) particularly for O3 and PM. The effect of green space was smaller than the pollutant deposition and dispersion effects of meteorological drivers including precipitation, humidity, and wind speed. When averaged across spatial scales, a one SD increase in green space resulted in a 0.8% (95% CI: -3.5 to 2%) decline in air pollution. Our findings suggest that while urban greening may improve air quality at the borough-to-city scale, the impact is moderate and may have detrimental street-level effects depending on aerodynamic factors like vegetation type and urban form.

Study on the Concentration of Top Air Pollutants in Xuzhou City in Winter 2020 Based on the WRF-Chem and ADMS-Urban Models

In recent years, the issue of air pollution has garnered significant public attention globally, with a particular emphasis on the challenge of atmospheric fine particulate matter (PM2.5) pollution. The efficient and precise simulation of changes in pollutant concentrations, as well as their spatial and temporal distribution, is essential for effectively addressing the air pollution issue. In this paper, the WRF-Chem model is used to simulate the meteorological elements including temperature (T), relative humidity (RH), wind speed (WS), and pressure (P), and the concentrations of PM2.5 and PM10 atmospheric pollutants in December 2020 in Xuzhou City. Simultaneously, the ADMS-Urban model was employed to conduct a higher spatial resolution study of PM2.5 concentrations during the heavy pollution days of 11–12 December 2020 in Xuzhou City. The study shows that the WRF-Chem model can simulate the meteorological conditions of the study time period better, and the correlation coefficients (R) of pressure, temperature, wind speed, and relative humidity are 0.99, 0.87, 0.75, and 0.70, respectively. The WRF-Chem model can accurately simulate the PM2.5 concentration on clean days (R of 0.66), but the simulation of polluted days is not satisfactory. Therefore, the ADMS-Urban model was chosen to simulate the PM2.5 concentration on polluted days in the center of Xuzhou City. The ADMS-Urban model can simulate the distribution characteristics and concentration changes of PM2.5 around roads and buildings in the center of Xuzhou City. Comparing the simulation results of the two models, it was found that the two models have their own advantages in PM2.5 concentration simulation, and how to better couple the two models is the next research direction.

Unravelling Bangalore's air quality during the second wave: Multifaceted analysis of COVID-19 lockdown impact

This study assesses the impact of the second wave COVID-19 lockdown (March–June 2021) on air pollution levels in Bangalore, India, using real-time data from eight CPCB monitoring stations across the city. This research endeavours to dissect the multifaceted impact of the 2021 s wave COVID-19 lockdown on Bangalore's air quality. Leveraging real-time data sourced from eight CPCB monitoring stations across the city, the study meticulously delves into diverse analyses to grasp the nuanced implications of the lockdown measures. It scrutinizes changes in six major pollutants: PM2.5, PM10, CO, NO2, SO2, and O3 during the 2021 lockdown compared to the same period in 2020. The comprehensive studies of different temporal scales were examined such as daily, weekly, and seasonal. The daily and weekly averages were computed to assess the percentage change in air pollutant concentrations during the March to June 2021 lockdown and pre-lockdown phases. The seasonal averages were derived to capture trends in seasonal variations. The examination of air pollution disparities across the city's eight monitoring stations (ST1 to ST8), unravelling localized variations and comprehending the spatial intricacies influencing air quality levels within Bangalore. The comparison of air pollution concentrations between the 2021 and 2020 lockdown periods, offering insights into relative changes in pollutant levels. The concentrations during the 2021 lockdown period (April 27 to June 14) significantly dropped compared to the pre-lockdown phase (March 9 to April 26) by considerable percentages across various pollutants: PM2.5 (45 %), PM10 (49 %), O3 (37 %), NO2 (41 %), SO2 (5 %), and CO (30 %). The comparative evaluation between air pollutant intensity changes during the common lockdown periods of the first wave (2020) and the second wave (2021) of COVID-19. This contiguity illuminates the shifts in pollution levels and underscores the differing dynamics of air quality between these distinct phases. This research provides comprehensive insights into air quality over Bangalore city during the second wave lockdown. The multifaceted analyses offer a detailed understanding of temporal, spatial, and comparative facets of air pollution dynamics, shedding light on the significant impact of COVID-19 restrictions on urban air quality.

Changes in industrial air pollution and the onset of childhood asthma in Quebec, Canada

Ambient air pollution has been associated with asthma onset and exacerbation in children. Whether improvement in air quality due to reduced industrial emissions has resulted in improved health outcomes such as asthma in some localities has usually been assessed indirectly with studies on between-subject comparisons of air pollution from all sources and health outcomes. In this study we directly assessed, within small areas in the province of Quebec (Canada), the influence of changes in local industrial fine particulate matter (PM2.5), nitrogen dioxide (NO2), and sulfur dioxide (SO2) concentrations, on changes in annual asthma onset rates in children (≤12 years old) with a longitudinal ecological design. We identified the yearly number of new cases of childhood asthma in 1282 small areas (census tracts or local community service centers) for the years 2002, 2004, 2005, 2006, and 2015. Annual average concentrations of industrial air pollutants for each of the geographic areas, and three sectors (i.e., pulp and paper mills, petroleum refineries, and metal smelters) were estimated by the Polair3D chemical transport model. Fixed-effects negative binomial models adjusted for household income were used to assess associations; additional adjustments for environmental tobacco smoke, background pollutant concentrations, vegetation coverage, and sociodemographic characteristics were conducted in sensitivity analyses. The incidence rate ratios (IRR) for childhood asthma onset for the interquartile increase in total industrial PM2.5, NO2, and SO2 were 1.016 (95% confidence interval, CI: 1.006–1.026), 1.063 (1.045–1.090), and 1.048 (1.031–1.080), respectively. Positive associations were also found with pollutant concentrations from most individual sectors. Results suggest that changes in industrial pollutant concentrations influence childhood asthma onset rates in small localities.

Study on the changes in concentration of air pollutants and influencing factors in road green spaces in Nanjing City during autumn and winter

Based on low-altitude remote sensing of UAV and field monitoring on three roads in Nanjing, this study used the normalized difference vegetation index (NDVI) one-dimensional linear regression analysis, correlation analysis to explore the relationship between different influencing factors and concentration changes of different pollutants in autumn and winter. The results showed that: 1. The use of UAV low-altitude remote sensing to collect vegetation data at road scale coould reflect the change characteristics of road vegetation in autumn and winter, especially the proportion of deciduous plants: among three roads, the ratio of deciduous plants to road sections showed that Mengdu Street < Mufu South Road < Jiyin Avenue; 2. Seasonal variation had a significant effect on temperature. In autumn, the concentration of NO2 in Jiyin Avenue and Mufu South Road exceeded the standard limit of 24 h average level 2 concentration, which meant the air pollution was relatively serious. 3. The increase of NDVI could reduce the concentration of atmospheric pollutants. Also, the study found that humidity had the greatest correlation with PM2.5 and PM10, temperature had the greatest correlation with NO2 and air pressure had the greatest correlation with O3. In addition, the four pollutants had positive correlation with each other.

Emission Analysis of Electric Motorcycles and Assessment of Emission Reduction With Fleet Electrification

The motorcycle is a common mode of transport in many countries. Although there exist abundant studies on emissions from internal combustion engine motorcycles, little is known regarding the emissions from electric motorcycles (EMs). Methods commonly used in emissions analysis for internal combustion engine motorcycles are not suitable for non-exhaust emissions produced from EMs. Computer vision could provide the opportunity to facilitate analysis of EMs’ emissions analysis by utilizing EM counts in traffic videos captured from surveillance cameras. The proposed approach incorporates computer vision-based counting of EMs and internal combustion engine motorcycles in combination with ambient pollutant concentration monitoring. The measured pollutant concentration could be apportioned to EMs based on its proportion in the traffic flow. Results show that coarse particle number and PM <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$_{1.0}$</tex-math> </inline-formula> are associated to the number of EMs with P Values smaller than 0.001. We also assess the potential change in ambient pollutant concentration through sensitivity analysis on motorcycle fleet electrification. Replacing all internal combustion engine motorcycles with the EMs could potentially reduce urban pollutant emissions by 48.62% for fine particles, 23.54% for CO <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$_{2}$</tex-math> </inline-formula> , and 13.41% for CO. However, this leads to increase of 29.83% for coarse particles and 24.09% for PM <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$_{1.0}$</tex-math> </inline-formula> . Through collected real-world data, the contributions of this study are the utilization of EM counts in emission analysis and the quantification of emission reduction by motorcycle electrification. This study could be applied for exposure assessment to emissions and their related health impacts to commuters.

Long-Term Dynamics of Persistent Organic Pollutants in Water Bodies of the Aral Sea–Syrdarya Basin

The huge decrease in water volume in the Aral Sea, which was caused predominately by enormous water withdrawals from the Aral Sea tributaries, has changed the former world’s fourth-biggest inland lake into a new desert called Aralkum. Due to its long-term use in agriculture, persistent organic pollutants (POPs), including organochlorine pesticides (OCPs), and polychlorinated biphenyls (PCBs) play a special role in the pollution of the Aral Sea tributaries and in the Aral Sea itself. For the first time, POPs, OCPs, and PCBs were studied in the Large Aral Sea, Small Aral Sea, and the Syrdarya River. PCBs and OCPs in the collected water samples were analyzed using gas chromatography. The results of long-term and inter-annual analyses of POPs in water resources of the Kazakh part of the Aral Sea–Syrdarya Basin are presented. The analyses covered the Large and Small Aral Seas, the Syrdarya River, reservoirs, lakes of the river delta, and drainage water of the collectors from irrigation areas. The inflow to the Syrdarya River that came from the collector–drainage water was the main cause of water pollution. The pollution levels of toxic compounds, including the trends of temporal and spatial changes in pollutant concentrations, were analyzed and assessed. The latest studies from 2021 and 2022 show a decrease in pesticide concentrations by tens and hundreds of times.

Estimation of health risk and economic loss attributable to PM2.5 and O3 pollution in Jilin Province, China

Ambient pollutants, particularly fine particulate matter (PM2.5) and ozone (O3), pose significant risks to both public health and economic development. In recent years, PM2.5 concentration in China has decreased significantly, whereas that of O3 has increased rapidly, leading to considerable health risks. In this study, a generalized additive model was employed to establish the relationship of PM2.5 and O3 exposure with non-accidental mortality across 17 districts and counties in Jilin Province, China, over 2015–2016. The health burden and economic losses attributable to PM2.5 and O3 were assessed using high-resolution satellite and population data. According to the results, per 10 µg/m3 increase in PM2.5 and O3 concentrations related to an overall relative risk (95% confidence interval) of 1.004 (1.001–1.007) and 1.009 (1.005–1.012), respectively. In general, the spatial distribution of mortality and economic losses was uneven. Throughout the study period, a total of 23,051.274 mortalities and 27,825.015 million Chinese Yuan (CNY) in economic losses were attributed to O3 exposure, which considerably surpassing the 5,450.716 mortalities and 6,553,780 million CNY in economic losses attributed to PM2.5 exposure. The O3-related health risks and economic losses increased by 3.75% and 9.3% from 2015 to 2016, while those linked to PM2.5 decreased by 23.33% and 18.7%. Sensitivity analysis results indicated that changes in pollutant concentrations were the major factors affecting mortality rather than baseline mortality and population.

Tracing air pollution changes (CO, NO2, SO2, and HCHO) using GEE and Sentinel 5P images in Ahvaz, Iran.

The first case of COVID-19 in Iran was reported on February 25, 2020, leading in the implementation of a government-mandated lockdown as the virus gradually spread to different cities. The objective of this study was to evaluate the impact of the COVID-19 pandemic on air quality in Ahvaz city by utilizing Sentinel 5 images and the Google Earth Engine (GEE) platform. Specifically, the concentrations of air pollutants, including CO, NO2, SO2, and HCHO, during the COVID-19 pandemic from May 10 to June 01, 2021, were examined. Also, they were compared to the same period in 2019. Additionally, the influence of meteorological parameters, such as wind speed and precipitation, on pollutant concentrations during the pandemic and in the pre-pandemic year of 2019 were investigated. The results revealed a significant decrease in the concentrations of NO2 (13.7%), CO (6.1%), SO2 (28%), and HCHO (9.5%) in Ahvaz during the study period in 2021 compared to the same period in 2019. Statistical analyses indicated no significant changes in wind speed and precipitation between the COVID-19 pandemic and the pre-pandemic period in 2019. Therefore, the impact of these parameters on the observed changes in pollutant concentrations can be disregarded. It is clear that the COVID-19 epidemic and the subsequent lockdown measures, including traffic restrictions and business closures, played a crucial role in significantly reducing air pollutant concentrations in Ahvaz.