Heterogeneous reaction of NO2 with hematite: The effects of UV irradiation, O2 and relative humidity

Heterogeneous reactions on mineral dust surfaces are increasingly considered important in the removal of gaseous pollutants and the formation of secondary aerosols. Although the heterogeneous reaction of NO2 on the hematite surface has been investigated in many previous studies, little is known about the reaction of NO2 with hematite under ambient conditions. In this work, heterogeneous reactions of NO2 with hematite at 298 K were investigated via a coated-wall flow tube reactor and in situ diffuse reflectance Fourier transformed infrared spectroscopy (DRIFTS). The influence of UV illumination, relative humidity (RH) and O2 on the uptake coefficients and adsorption amount of NO2, as well as the nitrate formation on the hematite surface, has been analyzed comprehensively. UV irradiation shows a significant effect on the true uptake coefficient (γBET), which increases from 2.00 × 10-6 to 4.76 × 10-6 in the N2 stream and 1.32 × 10-6 to 4.07 × 10-6 in the air stream under dry conditions (∼0.3 % RH). RH (in the range of 0-67 %) exhibits an inhibitory effect on the adsorption of NO2 on the hematite surface because of the competition between NO2 and water molecules, that is, γBET and adsorption amount of NO2 decrease with an increase in RH under both the dark and light reaction conditions. Meanwhile, both the γBET and adsorption amount of NO2 on hematite decrease in the air stream compared to those in N2 conditions. In addition, the results from the DRIFTS experiments indicate that the presence of UV irradiation promotes the conversion of NO2 to nitrate and both the RH and O2 suppress the nitrate formation. From this research, the heterogeneous reactions between NO2 with mineral dust under ambient conditions will be better understood.

A unified framework for assessing interaction effects among environmental exposures in epidemiologic studies: A case study on temperature, air pollution, and kidney-related conditions in New York state

BACKGROUND

There are various methods to assess interaction effects. However, current methods have limitations, and quantification of interaction effects is rarely performed. This study aimed to develop a unified quantitative framework for assessing interaction effects.

METHODS

We proposed a novel framework using log-linear models with a product term(s) across the exposures that generates parametric bi-variate association and interaction effect surfaces and allows flexible functional forms for exposures in the interaction term(s). In a case study, we assessed the interaction effects between temperature and air pollution (i.e., PM2.5, NO2, and O3) on risk for kidney-related conditions in New York State (2007-2016) using a case-crossover design with conditional logistic models. Our measures of exposure were the moving averages at lag 0-5 days for air pollution (linear) and daytime mean outdoor wet-bulb globe temperature (WBGT; using a natural cubic spline).

RESULTS

We derived closed-form expressions for the magnitude of multiplicative interaction effects (the joint relative risk divided by the product of the two conditional relative risks) and their uncertainties. In the case study, we found a Bonferroni-corrected significant multiplicative interaction effect (IE) between outdoor WBGT at the 99th percentile (median as the reference) and (1) PM2.5 (per 5 μg/m3 increase, IE = 1.052; 95 % confidence interval [CI]: 1.019, 1.087) for acute kidney failure and (2) O3 (per 5 ppb increase; IE = 1.022; 95 % CI: 1.008, 1.036) for urolithiasis (the latter being inconclusive based on the sensitivity analysis).

CONCLUSIONS

Our framework allows different functional forms of exposure variables in the interaction term, quantifies the magnitudes of entire-exposure-range (in addition to discrete exposure level) multiplicative interaction effects and their uncertainties in a categorical or continuous (linear or non-linear) manner, and harmonizes the two-way evaluation of effect modification. The case study underscores co-consideration of heat and air pollution when estimating health burden and designing heat/pollution alert systems.

Outdoor air pollution as a risk factor for testing positive for SARS-CoV-2: A nationwide test-negative case-control study in the Netherlands

Air pollution is a known risk factor for several diseases, but the extent to which it influences COVID-19 compared to other respiratory diseases remains unclear. We performed a test-negative case-control study among people with COVID-19-compatible symptoms who were tested for SARS-CoV-2 infection, to assess whether their long- and short-term exposure to ambient air pollution (AAP) was associated with testing positive (vs. negative) for SARS-CoV-2. We used individual-level data for all adult residents in the Netherlands who were tested for SARS-CoV-2 between June and November 2020, when only symptomatic people were tested, and modeled ambient concentrations of PM10, PM2.5, NO2 and O3 at geocoded residential addresses. In long-term exposure analysis, we selected individuals who did not change residential address in 2017-2019 (1.7 million tests) and considered the average concentrations of PM10, PM2.5 and NO2 in that period, and different sources of PM (industry, livestock, other agricultural activities, road traffic, other Dutch sources, foreign sources). In short-term exposure analysis, individuals not changing residential address in the two weeks before testing day (2.7 million tests) were included in the analyses, thus considering 1- and 2-week average concentrations of PM10, PM2.5, NO2 and O3 before testing day as exposure. Mixed-effects logistic regression analysis with adjustment for several confounders, including municipality and testing week to account for spatiotemporal variation in viral circulation, was used. Overall, there was no statistically significant effect of long-term exposure to the studied pollutants on the odds of testing positive vs. negative for SARS-CoV-2. However, significant positive associations of long-term exposure to PM10 and PM2.5 from specifically foreign and livestock sources, and to PM10 from other agricultural sources, were observed. Short-term exposure to PM10 (adjusting for NO2) and PM2.5 were also positively associated with increased odds of testing positive for SARS-CoV-2. While these exposures seemed to increase COVID-19 risk relative to other respiratory diseases, the underlying biological mechanisms remain unclear. This study reinforces the need to continue to strive for better air quality to support public health.

LUR modeling of long-term average hourly concentrations of NO2 using hyperlocal mobile monitoring data

Mobile monitoring campaigns have effectively captured spatial hyperlocal variations in long-term average concentrations of regulated and unregulated air pollutants. However, their application in estimating spatiotemporally varying maps has rarely been investigated. Tackling this gap, we investigated whether mobile measurements can assess long-term average nitrogen dioxide (NO2) concentrations for each hour of the day. Using mobile NO2 data monitored for 10 months in Amsterdam, we examined the performance of two spatiotemporal land use regression (LUR) methods, Spatiotemporal-Kriging and GTWR (Geographical and Temporal Weighted Regression), alongside two classical spatial LUR models developed separately for each hour. We found that mobile measurements follow the general pattern of fixed-site measurements, but with considerable deviations (indicating collection uncertainty). Leveraging heterogeneous spatiotemporal autocorrelations, GTWR smoothed these deviations and achieved an overall performance of an R2 of 0.49 and a Mean Absolute Error of 6.33 μg/m3, validated by long-term fixed-site measurements (out-of-sample). The other models tested were more affected by the collection uncertainty. We highlighted that the spatiotemporal variations captured in mobile measurements can be used to reconstruct long-term average hourly air pollution maps. These maps facilitate dynamic exposure assessments considering spatiotemporal human activity patterns.

Temporal characteristics and health effects related to NO2, O3, and SO2 in an urban area of Iran

This study reports on temporal variations of NO2, O3, and SO2 pollutants and their related health effects in urban air of Khorramabad, Iran using AirQ 2.2.3 software. Based on data between 2015 and 2021, hourly NO2, O3, and SO2 concentrations increase starting at 6:00 a.m. local time until 9:00 p.m., 3:00 p.m., and 7:00 p.m. local time, respectively, before gradually decreasing. The highest monthly NO2, O3, and SO2 concentrations are observed in October, August, and September, respectively. Annual median NO2, O3, and SO2 concentrations range between 17 ppb and 38.8 ppb, 17.5 ppb-36.6 ppb, and ∼14 ppb-30.8 ppb, respectively. Two to 93 days and 17-156 days between 2015 and 2021 exhibit daily concentrations of NO2 and SO2 ≤ WHO AQGs, respectively, while 187-294 days have 8-h maximum O3 concentrations ≤ WHO AQGs. The mean excess mortality ascribed to respiratory mortality, cardiovascular mortality, hospital admissions for COPD, and acute myocardial infraction are 121, 603, 39, and 145 during 2015-2021, respectively. O3 is found to exert more significant health effects compared to SO2 and NO2, resulting in higher cardiovascular mortality. The gradual increase in NO2 and possibly O3 over the study period is suspected to be due to economic sanctions, while SO2 decreased due to regulatory activity. Sustainable control strategies such as improving fuel quality, promoting public transportation and vehicle retirement, applying subsidies for purchase of electric vehicles, and application of European emission standards on automobiles can help decrease target pollutant levels in ambient air of cities in developing countries.

Prenatal air pollution exposure is associated with inflammatory, cardiovascular, and metabolic biomarkers in mothers and newborns

BACKGROUND

Prenatal air pollution exposure has been associated with individual inflammatory, cardiovascular, and metabolic biomarkers in mothers and neonates. However, studies of air pollution and a comprehensive panel of biomarkers across maternal and cord blood samples remain limited. Few studies used data-driven methods to identify biomarker groupings that converge biomarkers from multiple biological pathways. This study aims to investigate the impacts of prenatal air pollution on groups of biomarkers in maternal and cord blood samples.

METHODS

In the Maternal And Developmental Risks from Environmental and Social Stressors (MADRES) cohort, 87 biomarkers were quantified from 45 trimester 1 maternal blood and 55 cord blood samples. Pregnancy and trimester 1-averaged concentrations of particulate matter ≤2.5 μm and ≤10 μm in diameter (PM2.5 and PM10), nitrogen dioxide (NO2), and ozone (O3) were estimated, using inverse distance squared weighted spatial interpolation from regulatory air monitoring stations. Traffic-related NOx was assessed using California Line Source Dispersion Model: freeway/highway roads, non-freeway major roads, non-freeway minor roads, and their sum as total NOx. Elastic Net (EN) regression within the rexposome R package was used to group biomarkers and assess their associations with air pollution.

RESULTS

In maternal samples, trimester 1-averaged PM10 was associated with elevated inflammation biomarkers and lowered cardiovascular biomarkers. NO2 exhibited positive associations with cardiovascular and inflammation markers. O3 was inversely associated with inflammation, metabolic, and cardiovascular biomarkers. In cord blood, pregnancy-averaged PM2.5 was associated with higher cardiovascular biomarkers and lower metabolic biomarkers. PM10 was associated with lower inflammation and higher cardiovascular biomarkers. Total and major road NOx was associated with lower cardiovascular biomarkers.

CONCLUSION

Prenatal air pollution exposure was associated with changes in biomarkers related to inflammation, cardiovascular, metabolic, cancer, and neurological function in both mothers and neonates. This study shed light on mechanisms by which air pollution can influence biological function during pregnancy.

Research on regional ozone prevention and control strategies in eastern China based on pollutant transport network and FNR

O3 pollution in China has worsened sharply in recent years, and O3 formation sensitivity (OFS) in many regions have gradually changed, with eastern China as the most typical region. This study constructed the transport networks of O3 and NO2 in different seasons from 2017 to 2020. The transport trends and the clustering formation patterns were summarized by analyzing the topological characteristics of the transport networks, and the patterns of OFS changes were diagnosed by analyzing the satellite remote sensing data. Based on that, the main clusters that each province or city belongs to in different pollutant transport networks were summarized and proposals for the inter-regional joint prevention and control were put forward. As the results showed, O3 transport activity was most active in spring and summer and least active in winter, while NO2 transport activity was most active in autumn and winter and least active in summer. OFS in summer mainly consisted of transitional regimes and NOx-limited regimes, while that in other seasons was mainly VOC-limited regimes. Notably, there was a significant upward trend in the proportion of transitional regimes and NOx-limited regimes in spring, autumn, and winter. For regions showing NOx-limited regime, areas with higher out-weighted degrees in the NO2 transport network should focus on controlling local NOx emissions, such as central regions in summer. For regions showing VOC-limited regime, areas with higher out-weighted degrees in the O3 transport network should focus on controlling local VOCs emissions, such as central and south-central regions in summer. For regions that belong to the same cluster and present the same OFS in each specific season, regional cooperative emission reduction strategies should be established to block important transmission paths and weaken regional pollution consistency.

Hierarchically porous ZnO derived from zeolitic imidazolate frameworks for high-sensitive MEMS NO2 sensor

Three-dimensional (3D) porous metal oxide nanomaterials with controllable morphology and well-defined pore size have attracted extensive attention in the field of gas sensing. Herein, hierarchically porous ZnO-450 was obtained simply by annealing Zeolitic Imidazolate Frameworks (ZIF-90) microcrystals at an optimal temperature of 450 °C, and the effect of annealing temperature on the formation of porous nanostructure was discussed. Then the as-obtained ZnO-450 was employed as sensing materials to construct a Micro-Electro-Mechanical System (MEMS) gas sensor for detecting NO2. The MEMS sensor based on ZnO-450 displays the excellent gas-sensing performances at a lower working temperature (190 °C), such as high response value (242.18% @ 10 ppm), fast response/recovery time (9/26 s) and ultralow limit of detection (35 ppb). The ZnO-450 sensor shows better sensing performance for NO2 detection than ZnO-based composites materials or commercial ZnO nanoparticles (NPs), which are attributed to its unique hierarchically structures with high porosity and larger surface area. This ZIFs driven strategy can be expected to pave a new pathway for the design of high-performance NO2 sensors.

Impact of particulate nitrate photolysis on air quality over the Northern Hemisphere

We use the Community Multiscale Air Quality (CMAQv5.4) model to examine the potential impact of particulate nitrate (pNO3-) photolysis on air quality over the Northern Hemisphere. We estimate the photolysis frequency of pNO3- by scaling the photolysis frequency of nitric acid (HNO3) with an enhancement factor that varies between 10 and 100 depending on pNO3- and sea-salt aerosol concentrations and then perform CMAQ simulations without and with pNO3- photolysis to quantify the range of impacts on tropospheric composition. The photolysis of pNO3- produces gaseous nitrous acid (HONO) and nitrogen dioxide (NO2) over seawater thereby increasing atmospheric HONO and NO2 mixing ratios. HONO subsequently undergoes photolysis, producing hydroxyl radicals (OH). The increase in NO2 and OH alters atmospheric chemistry and enhances the atmospheric ozone (O3) mixing ratio over seawater, which is subsequently transported to downwind continental regions. Seasonal mean model O3 vertical column densities without pNO3- photolysis are lower than the Ozone Monitoring Instrument (OMI) retrievals, while the column densities with the pNO3- photolysis agree better with the OMI retrievals of tropospheric O3 burden. We compare model O3 mixing ratios with available surface observed data from the U.S., Japan, the Tropospheric Ozone Assessment Report - Phase II, and OpenAQ; and find that the model without pNO3- photolysis underestimates the observed data in winter and spring seasons and the model with pNO3- photolysis improves the comparison in both seasons, largely rectifying the pronounced underestimation in spring. Compared to measurements from the western U.S., model O3 mixing ratios with pNO3- photolysis agree better with observed data in all months due to the persistent underestimation of O3 without pNO3- photolysis. Compared to the ozonesonde measurements, model O3 mixing ratios with pNO3- photolysis also agree better with observed data than the model O3 without pNO3- photolysis.

Using dispersion models at microscale to assess long-term air pollution in urban hot spots: A FAIRMODE joint intercomparison exercise for a case study in Antwerp

In the framework of the Forum for Air Quality Modelling in Europe (FAIRMODE), a modelling intercomparison exercise for computing NO2 long-term average concentrations in urban districts with a very high spatial resolution was carried out. This exercise was undertaken for a district of Antwerp (Belgium). Air quality data includes data recorded in air quality monitoring stations and 73 passive samplers deployed during one-month period in 2016. The modelling domain was 800 × 800 m2. Nine modelling teams participated in this exercise providing results from fifteen different modelling applications based on different kinds of model approaches (CFD - Computational Fluid Dynamics-, Lagrangian, Gaussian, and Artificial Intelligence). Some approaches consisted of models running the complete one-month period on an hourly basis, but most others used a scenario approach, which relies on simulations of scenarios representative of wind conditions combined with post-processing to retrieve a one-month average of NO2 concentrations. The objective of this study is to evaluate what type of modelling system is better suited to get a good estimate of long-term averages in complex urban districts. This is very important for air quality assessment under the European ambient air quality directives. The time evolution of NO2 hourly concentrations during a day of relative high pollution was rather well estimated by all models. Relative to high resolution spatial distribution of one-month NO2 averaged concentrations, Gaussian models were not able to give detailed information, unless they include building data and street-canyon parameterizations. The models that account for complex urban geometries (i.e. CFD, Lagrangian, and AI models) appear to provide better estimates of the spatial distribution of one-month NO2 averages concentrations in the urban canopy. Approaches based on steady CFD-RANS (Reynolds Averaged Navier Stokes) model simulations of meteorological scenarios seem to provide good results with similar quality to those obtained with an unsteady one-month period CFD-RANS simulations.

The impact of air pollutants on spontaneous abortion: a case-control study in Tongchuan City

OBJECTIVES

Studies related to air pollutants and spontaneous abortion in urban northwestern China are scarce, and the main exposure windows of pollutants acting on pregnant women are unclear.

STUDY DESIGN

Case-control study.

METHODS

Data were collected from pregnant women in Tongchuan City from 2018 to 2019. A total of 289 cases of spontaneous abortion and 1156 cases of full-term labor were included and analyzed using a case-control study. Logistic regression models were developed to explore the relationship between air pollutants and spontaneous abortion after Chi square analysis and Air pollutant description.

RESULTS

O3 (odds ratio [OR] = 1.028) is a risk factor for spontaneous abortion throughout pregnancy. PM2.5 (OR = 1.015), PM10 (OR = 1.010), SO2 (OR = 1.026), and NO2 (OR = 1.028) are risk factors for spontaneous abortion in the 30 days before the last menstrual period. PM2.5 (OR = 1.015), PM10 (OR = 1.013), SO2 (OR = 1.036), and NO2 (OR = 1.033) are risk factors for spontaneous abortion in the 30-60 days before the last menstrual period. PM2.5 (OR = 1.028), PM10 (OR = 1.013), SO2 (OR = 1.035), and NO2 (OR = 1.059) are risk factors for spontaneous abortion in the 60-90 days before the last menstrual period.

CONCLUSION

Exposure to high levels of air pollutants may be a cause of increased risk of spontaneous abortion, especially in the first trimester of the last menstrual period.

Modeling NO2 air pollution variation during and after COVID-19-regulation using principal component analysis of satellite imagery

By implementing Principal Component Analysis (PCA) of multitemporal satellite data, this paper presents modeling solutions for air pollutant variation in three scenarios related to COVID-19 lockdown: pre, during, and after lockdown. Tropospheric NO2 satellite data from Sentinel-5P was used. Two novel PCA-models were developed: Weighted Principal Component Analysis (WPCA) and Rescaled Principal Component Analysis (RPCA). Model results were tested for goodness-of-fit to empirical NO2 data. The models were used to predict actual near-surface NO2 concentrations. Model-predicted NO2 concentrations were validated with NO2 data acquired at ground monitoring stations. Besides, meteorological bias affecting NO2 was assessed. It was found that the weather component had substantial impact on NO2 built-ups, propitiating air pollutant decrease during lockdown and increase after. WPCA and RPCA models well fitted to observed NO2. Both models accurately estimated near-surface NO2 concentrations. Modeled NO2 variation results evidenced the prolongated effect of the total lockdown (up to half a year). Model-predicted NO2 concentrations were found to highly correlate with monitoring station NO2 data collected on the ground. It is concluded that PCA is reliable in identifying and predicting air pollution variation patterns. The implementation of PCA is recommended when analyzing other pollutant gases.

Quantifying climate variation and associated regional air pollution in southern India using Google Earth Engine

Climate change and regional air pollution have had significant proportional coherence and are collectively hazardous for the regional ecosystem. To conduct this present investigation, we obtained high-resolution remotely sensed datasets from 2001 to 2022. To estimate climate variation, we utilized Climate Hazard Group InfraRed Precipitation with Station Data Version 2.0 (CHIRPS) and Moderate Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST). Additionally, we used Sentinel-5P datasets to collect spatio-temporal information for regional CO (Carbon Monoxide), NO2 (Nitrogen Dioxide), SO2 (Sulfur Dioxide), and UV Aerosol index for Coimbatore city. Numerous non-parametric and descriptive statistical applications were then employed to check the spatial integrity of satellite data products and spatio-temporal trends using Google Earth Engine algorithms. The study reveals most of the southern parts of Coimbatore city witnessed increased LST (0.10 °C/year) together with decreased rainfall (21.5 mm/year). Moreover, regional concentration of air pollutants exhibits spatio-temporal variability at annual and seasonal scales, where maximum engrossment is occupied by CO during the pre-monsoon and monsoon season. However, other pollutants are also dominant in the northern parts of the city, whereas NO2 and absorbing Aerosol during pre-monsoon season experienced significant increase throughout the years. Understanding the fluctuations in air pollution levels across different weather situations might help in developing targeted pollution reduction methods.

Long-term spatiotemporal variations in surface NO2 for Beijing reconstructed from surface data and satellite retrievals

Remote sensing data from the Ozone Monitoring Instrument (OMI) and the TROPOspheric Monitoring Instrument (TROPOMI) play important roles in estimating surface nitrogen dioxide (NO2), but few studies have compared their differences for application in surface NO2 reconstruction. This study aims to explore the effectiveness of incorporating the tropospheric NO2 vertical column density (VCD) from OMI and TROPOMI (hereafter referred to as OMI and TROPOMI, respectively, for conciseness) for deriving surface NO2 and to apply the resulting data to revisit the spatiotemporal variations in surface NO2 for Beijing over the 2005-2020 period during which there were significant reductions in nitrogen oxide emissions. In the OMI versus TROPOMI performance comparison, the cross-validation R2 values were 0.73 and 0.72, respectively, at 1 km resolution and 0.69 for both at 100 m resolution. The comparisons between satellite data sources indicate that even though TROPOMI has a finer resolution it does not improve upon OMI for deriving surface NO2 at 1 km resolution, especially for analyzing long-term trends. In light of the comparison results, we used a hybrid approach based on machine learning to derive the spatiotemporal distribution of surface NO2 during 2005-2020 based on OMI. We had novel, independent passive sampling data collected weekly from July to September of 2008 for hindcasting validation and found a spatiotemporal R2 of 0.46 (RMSE = 7.0 ppb). Regarding the long-term trend of surface NO2, the level in 2008 was obviously lower than that in 2007 and 2009, as expected, which was attributed to pollution restrictions during the Olympic Games. The NO2 level started to steadily decline from 2015 and fell below 2008's level after 2017. Based on OMI, a long-term and fine-resolution surface NO2 dataset was developed for Beijing to support future environmental management questions and epidemiological research.

McPrA - A new gas profile inversion algorithm for MAX-DOAS and apply to 50 m vertical resolution

Air pollutants represent an environmental and health risk, and the methods for their effective assessment are of the greatest importance. The MAX-DOAS method is a reliable retrieval algorithm, enabling a vertical gas profile analysis. However, the current MAX-DOAS retrieval algorithm heavily relies on the a priori profile, limiting its accuracy. To address this issue, we introduced a novel MAX-DOAS trace gas profile inversion algorithm called McPrA, which is less dependent on the a priori profile. It employs the Monte Carlo method to resolve the problem of optimal estimation of trace gases. The gas vertical column density is obtained from the air mass factor calculated by SCIATRAN. Afterward, the trace gas vertical distribution is retrieved by combining the weight function with the a priori profile. A normalization process is also included to improve the matching of the weight function and the a priori profile. The McPrA algorithm enables greater flexibility in grid modification to achieve a higher vertical resolution of up to 50 m, while sensitivity experiments contribute to determining the optimal configuration of retrieval parameters, with a degree of freedom of over 3.0. Comparative verification experiments indicate that the McPrA algorithm accurately retrieves gas profiles, with a correlation coefficient of over 0.89 for NO2 in the first layer compared to in situ data. Furthermore, comparisons with WRF-Chem and the simulation of synthetic data demonstrate the effectiveness of the McPrA algorithm in accurately retrieving gas profiles.

In-vehicle exposure to NO2 and PM2.5: A comprehensive assessment of controlling parameters and reduction strategies to minimise personal exposure

Vehicles are the third most occupied microenvironment, other than home and workplace, in developed urban areas. Vehicle cabins are confined spaces where occupants can mitigate their exposure to on-road nitrogen dioxide (NO2) and fine particulate matter (PM2.5) concentrations. Understanding which parameters exert the greatest influence on in-vehicle exposure underpins advice to drivers and vehicle occupants in general. This study assessed the in-vehicle NO2 and PM2.5 levels and developed stepwise general additive mixed models (sGAMM) to investigate comprehensively the combined and individual influences of factors that influence the in-vehicle exposures. The mean in-vehicle levels were 19 ± 18 and 6.4 ± 2.7 μg/m3 for NO2 and PM2.5, respectively. sGAMM model identified significant factors explaining a large fraction of in-vehicle NO2 and PM2.5 variability, R2 = 0.645 and 0.723, respectively. From the model's explained variability on-road air pollution was the most important predictor accounting for 22.3 and 30 % of NO2 and PM2.5 variability, respectively. Vehicle-based predictors included manufacturing year, cabin size, odometer reading, type of cabin filter, ventilation fan speed power, window setting, and use of air recirculation, and together explained 48.7 % and 61.3 % of NO2 and PM2.5 variability, respectively, with 41.4 % and 51.9 %, related to ventilation preference and type of filtration media, respectively. Driving-based parameters included driving speed, traffic conditions, traffic lights, roundabouts, and following high emitters and accounted for 22 and 7.4 % of in-vehicle NO2 and PM2.5 exposure variability, respectively. Vehicle occupants can significantly reduce their in-vehicle exposure by moderating vehicle ventilation settings and by choosing an appropriate cabin air filter.

NO2 emissions from oil refineries in the Mississippi Delta

Of the >17,943 thousand barrels per calendar day (bbl/d) of oil refining capacity located in the US, the Petroleum Administration for Defense District 3 (PADD-3) region has the largest number of refineries and accounts for >53 % (or 9607 tbbl/d) of all US oil refining capacity. Processing facilities in this area are mainly located on the Gulf of Mexico coast in Texas and Louisiana. This study selected a sub-region for analysis within the Mississippi River delta in the state of Louisiana between the cities of New Orleans and Baton Rouge. This region is characterized by intensive industrial activity connected with oil refining and related activities. The TROPOspheric Monitoring Instrument (TROPOMI) detected highly localized NO2 vertical column densities (VCDs) over the two largest US refineries in Baton Rouge (503,000 bbl/d) and Garyville (578,000 bbl/d). TROPOMI NO2 VCD over these stations were 100 μmol/m2 and 80 μmol/m2, respectively. A high correlation coefficient (r = 0.65, p < 0.05) was also found between TROPOMI NO2 and population density. Data from the National Emissions Inventory (NEI) showed high NOx emissions from refineries and other industries including coal-fired power generation, chemical, and aluminum processing plants. The results of the NO2 analysis are of practical interest for a comparative assessment of air pollution, as well as for the exchange of best practices in the field of low-waste fuel combustion technologies.

Intimately tracking NO2 pollution over the New York City - Long Island Sound land-water continuum: An integration of shipboard, airborne, satellite observations, and models

Nitrogen dioxide (NO2) pollution remains a serious global problem, particularly near highly populated urbanized coasts that face increasing challenges with climate change. Yet, the combined impact of urban emissions, pollution transport, and complex meteorology on the spatiotemporal dynamics of NO2 along heterogeneous urban coastlines remains poorly characterized. Here, we integrated measurements from different platforms - boats, ground-based networks, aircraft, and satellites - to characterize total column NO2 (TCNO2) dynamics across the land-water continuum in the New York metropolitan area, the most populous area in the United States that often experiences the highest national NO2 levels. Measurements were conducted during the 2018 Long Island Sound Tropospheric Ozone Study (LISTOS), with a main goal to extend surface measurements beyond the coastline - where ground-based air-quality monitoring networks abruptly stop - and over the aquatic environment where peaks in air pollution often occur. Satellite TCNO2 from TROPOMI correlated strongly with Pandora surface measurements (r = 0.87, N = 100) both over land and water. Yet, TROPOMI overall underestimated TCNO2 (MPD = -12%) and missed peaks in NO2 pollution caused by rush hour emissions or pollution accumulation during sea breezes. Aircraft retrievals were in excellent agreement with Pandora (r = 0.95, MPD = -0.3%, N = 108). Stronger agreement was found between TROPOMI, aircraft, and Pandora over land, while over water satellite, and to a lesser extent aircraft, retrievals underestimated TCNO2 particularly in the highly dynamic New York Harbor environment. Combined with model simulations, our shipborne measurements uniquely captured rapid transitions and fine-scale features in NO2 behavior across the New York City - Long Island Sound land-water continuum, driven by the complex interplay of human activity, chemistry, and local scale meteorology. These novel datasets provide critical information for improving satellite retrievals, enhancing air quality models, and informing management decisions, with important implications for the health of diverse communities and vulnerable ecosystems along this complex urban coastline.

Prenatal and childhood exposure to ambient air pollution and cognitive function in school-age children: Examining sensitive windows and sex-specific associations

BACKGROUND

Combined effect of both prenatal and early postnatal exposure to ambient air pollution on child cognition has rarely been investigated and periods of sensitivity are unknown. This study explores the temporal relationship between pre- and postnatal exposure to PM10, PM2.5, NO2 and child cognitive function.

METHODS

Using validated spatiotemporally resolved exposure models, pre- and postnatal daily PM2.5, PM10 (satellite based, 1 km resolution) and NO2 (chemistry-transport model, 4 km resolution) concentrations at the mother's residence were estimated for 1271 mother-child pairs from the French EDEN and PELAGIE cohorts. Scores representative of children's General, Verbal and Non-Verbal abilities at 5-6 years were constructed based on subscale scores from the WPPSI-III, WISC-IV or NEPSY-II batteries, using confirmatory factor analysis (CFA). Associations of both prenatal (first 35 gestational weeks) and postnatal (60 months after birth) exposure to air pollutants with child cognition were explored using Distributed Lag Non-linear Models adjusted for confounders.

RESULTS

Increased maternal exposure to PM10, PM2.5 and NO2, during sensitive windows comprised between the 15th and the 33rd gestational weeks, was associated with lower males' General and Non-verbal abilities. Higher postnatal exposure to PM2.5 between the 35th and 52nd month of life was associated with lower males' General, Verbal and Non-verbal abilities. Some protective associations were punctually observed for the very first gestational weeks or months of life for both males and females and the different pollutants and cognitive scores.

DISCUSSION

These results suggest poorer cognitive function at 5-6 years among males following increased maternal exposure to PM10, PM2.5 and NO2 during mid-pregnancy and child exposure to PM2.5 around 3-4 years. Apparent protective associations observed are unlikely to be causal and might be due to live birth selection bias, chance finding or residual confounding.

Classification and concentration estimation of CO and NO2 mixtures under humidity using neural network-assisted pattern recognition analysis

This study addresses the concerns regarding the cross-sensitivity of metal oxide sensors by building an array of sensors and subsequently utilizing machine earning techniques to analyze the data from the sensor arrays. Sensors were built using In2O3, Au-ZnO, Au-SnO2, and Pt-SnO2 and they were operated simultaneously in the presence of 25 different concentrations of nitrogen dioxide (NO2), carbon monoxide (CO), and their mixtures. To investigate the effects of humidity, experiments were conducted to detect 13 distinct CO and NO2 gas combinations in atmospheres with 40% and 90% relative humidity. Principal component analysis was performed for the normalized resistance variation collected for a particular gas atmosphere over a certain period, and the results were used to train deep neural network-based models. The dynamic curves produced by the sensor array were treated as pixelated images and a convolutional neural network was adopted for classification. An accuracy of 100% was achieved using both models during cross-validation and testing. The results indicate that this novel approach can eliminate the time-consuming feature extraction process.

Comparison of the heterogeneous reaction of NO2 on the surface of clay minerals and desert dust particles

Mineral particles in air could provide atmospheric chemical reaction interface for gaseous substances and participate in atmospheric chemical reaction process, and affecting the status and levels of gaseous pollutants in air. However, differences of the heterogenous reaction on the surface minerals particles are not very clear. Considering main mineral composition of ambient particles was from dust emission, therefore, typical clay minerals (chlorite, illite) and desert particles (Taklimakan Desert) were selected to analysize chemical reaction of NO2, one of major gaseous pollutants, on mineral particles by using of In-situ DRIFTS (diffuse reflectance infrared Fourier transform spectroscopy) under different condition. And In situ near-ambient pressure X-ray photoelectron spectroscopy (In situ NAP-XPS) was employed to investigate iron (one of the major metals) species variation on the surface of mineral dust particles during the heterogeneous reactions. Our data show that humidity controlled by deuterium oxide (D2O) has a greater effect on chemical reactions compared to light and temperature. Under dry conditions, the amount of heterogeneous reaction products of NO2 on the particles shows Xiaotang dust > chlorite > illite > Tazhong dust regardless of dark or light conditions. In contrast, under humidity conditions, the order of nitrate product quantity under moderate conditions was chlorite > illite > Xiaotang dust > Tazhong dust. In situ NAP-XPS results demonstrate that specie variation of the Fe could promote the heterogenous reactions. These data could provide useful information for understanding the formation mechanism of nitrate aerosols and removal of nitrogen oxides in the atmosphere.

Associations between short-term exposure to PM2.5, NO2 and O3 pollution and kidney-related conditions and the role of temperature-adjustment specification: A case-crossover study in New York state

Epidemiologic evidence on the relationship between air pollution and kidney disease remains inconclusive. We evaluated associations between short-term exposure to PM_2.5, NO₂ and O₃ and unplanned hospital visits for seven kidney-related conditions (acute kidney failure [AKF], urolithiasis, glomerular diseases [GD], renal tubulo-interstitial diseases, chronic kidney disease, dysnatremia, and volume depletion; n = 1,209,934) in New York State (2007-2016). We applied a case-crossover design with conditional logistic regression, controlling for temperature, dew point temperature, wind speed, and solar radiation. We used a three-pollutant model at lag 0-5 days of exposure as our main model. We also assessed the influence of model adjustment using different specifications of temperature by comparing seven temperature metrics (e.g., dry-bulb temperature, heat index) and five intraday temperature measures (e.g., daily mean, daily minimum, nighttime mean), according to model performance and association magnitudes between air pollutants and kidney-related conditions. In our main models, we adjusted for daytime mean outdoor wet-bulb globe temperature, which showed good model performance across all kidney-related conditions. We observed the odds ratios (ORs) for 5 μg/m³ increase in daily mean PM_2.5 to be 1.013 (95% confidence interval [CI]: 1.001, 1.025) for AKF, 1.107 (95% CI: 1.018, 1.203) for GD, and 1.027 (95% CI: 1.015, 1.038) for volume depletion; and the OR for 5 ppb increase in daily 1-hour maximum NO₂ to be 1.014 (95% CI; 1.008, 1.021) for AKF. We observed no associations with daily 8-hour maximum O₃ exposure. Association estimates varied by adjustment for different intraday temperature measures: estimates adjusted for measures with poorer model performance resulted in the greatest deviation from estimates adjusted for daytime mean, especially for AKF and volume depletion. Our findings indicate that short-term exposure to PM_2.5 and NO₂ is a risk factor for specific kidney-related conditions and underscore the need for careful adjustment of temperature in air pollution epidemiologic studies.

A critical review and prospect of NO2 and SO2 pollution over Asia: Hotspots, trends, and sources

Nitrogen dioxide (NO₂) and sulfur dioxide (SO₂) are two major atmospheric pollutants that significantly threaten human health, the environment, and ecosystems worldwide. Despite this, only some studies have investigated the spatiotemporal hotspots of NO₂ and SO₂, their trends, production, and sources in Asia. Our study presents a literature review covering the production, trends, and sources of NO₂ and SO₂ across Asian countries (e.g., Bangladesh, China, India, Iran, Japan, Pakistan, Malaysia, Kuwait, and Nepal). Based on the findings of the review, NO₂ and SO₂ pollution are increasing due to industrial activity, fossil fuel burning, biomass burning, heavy traffic movement, electricity generation, and power plants. There is significant concern about health risks associated with NO₂ and SO₂ emissions in Bangladesh, China, India, Malaysia, and Iran, as they pay less attention to managing and controlling pollution. Even though the lack of quality datasets and adequate research in most Asian countries further complicates the management and control of NO₂ and SO₂ pollution. This study has NO₂ and SO₂ pollution scenarios, including hotspots, trends, sources, and their influences on Asian countries. This study highlights the existing research gaps and recommends new research on identifying integrated sources, their variations, spatiotemporal trends, emission characteristics, and pollution level. Finally, the present study suggests a framework for controlling and monitoring these two pollutants' emissions.

Associations of prenatal exposure to NO2 and near roadway residence with placental gene expression

INTRODUCTION

Traffic-related air pollution (TRAP), a common exposure, potentially impacts pregnancy through altered placental function. We investigated associations between prenatal TRAP exposure and placental gene expression.

METHODS

Whole transcriptome sequencing was performed on placental samples from CANDLE (Memphis, TN) (n = 776) and GAPPS (Seattle and Yakima, WA) (n = 205), cohorts of the ECHO-PATHWAYS Consortium. Residential NO₂ exposures were computed via spatiotemporal models for full-pregnancy, each trimester, and the first/last months of pregnancy. Individual cohort-specific, covariate-adjusted linear models were fit for 10,855 genes and respective exposures (NO₂ or roadway proximity [≤150 m]). Infant-sex/exposure interactions on placental gene expression were tested with interaction terms in separate models. Significance was based on false discovery rate (FDR<0.10).

RESULTS

In GAPPS, final-month NO₂ exposure was positively associated with MAP1LC3C expression (FDR p-value = 0.094). Infant-sex interacted with second-trimester NO₂ on STRIP2 expression (FDR interaction p-value = 0.011, inverse and positive associations among male and female infants, respectively) and roadway proximity on CEBPA expression (FDR interaction p-value = 0.045, inverse among females). In CANDLE, infant-sex interacted with first-trimester and full-pregnancy NO₂ on RASSF7 expression (FDR interaction p-values = 0.067 and 0.013, respectively, positive among male infants and inverse among female infants).

DISCUSSION

Overall, pregnancy NO₂ exposure and placental gene expression associations were primarily null, with exception of final month NO₂ exposure and placental MAP1LC3C association. We found several interactions of infant sex and TRAP exposures on placental expression of STRIP2, CEBPA, and RASSF7. These highlighted genes suggest influence of TRAP on placental cell proliferation, autophagy, and growth, though additional replication and functional studies are required for validation.

Filter blot method: A simple method for measuring 3-nitrotyrosine in proteins of atmospheric particulate matter

Air pollutants, such as nitrogen dioxide (NO₂), ozone (O₃), and particulate matter (PM), have been epidemiologically reported to contribute to the onset and exacerbation of asthma. We have previously shown that several proteins in atmospheric PM are allergenic in mouse asthma models and that these proteins are nitrated by atmospheric NO₂ and O₃ in chemical reactions. Based on these results, the amount of 3-nitrotyrosine (3-NT) in atmospheric PM could be an air pollution marker integrating NO₂, O₃, and PM. We established a method to measure 3-NT by high-performance liquid chromatography electrochemical detection (HPLC-ECD). Although this method is accurate, it requires a filter treatment process, which is time-consuming and costly for an environmental monitoring tool, in which many samples are measured simultaneously. Therefore, in this study, we investigated a simple immunoblotting method in which atmospheric PM proteins were directly transferred to a polyvinylidene fluoride (PVDF) membrane and measured using an anti-3-NT antibody (the filter blot method). The 3-NT value obtained from this method was significantly correlated (r = 0.809, p < 0.001) with that of the HPLC-ECD method, with a detection power of 0.1 μg/mL for tyrosine nitrated bovine serum albumin equivalents. Multiple regression analysis using the filter blot method showed that the amount of 3-NT in atmospheric PM was significantly associated with the published environmental measurements of O₃ and PM in the region. Therefore, the filter blot method may be useful for the environmental monitoring of 3-NT in atmospheric PM.

Peculiar weather patterns effects on air pollution and COVID-19 spread in Tokyo metropolis

As a pandemic hotspot in Japan, between March 1, 2020-October 1, 2022, Tokyo metropolis experienced seven COVID-19 waves. Motivated by the high rate of COVID-19 incidence and mortality during the seventh wave, and environmental/health challenges we conducted a time-series analysis to investigate the long-term interaction of air quality and climate variability with viral pandemic in Tokyo. Through daily time series geospatial and observational air pollution/climate data, and COVID-19 incidence and death cases, this study compared the environmental conditions during COVID-19 multiwaves. In spite of five State of Emergency (SOEs) restrictions associated with COVID-19 pandemic, during (2020-2022) period air quality recorded low improvements relative to (2015-2019) average annual values, namely: Aerosol Optical Depth increased by 9.13% in 2020 year, and declined by 6.64% in 2021, and 12.03% in 2022; particulate matter PM2.5 and PM10 decreased during 2020, 2021, and 2022 years by 10.22%, 62.26%, 0.39%, and respectively by 4.42%, 3.95%, 5.76%. For (2021-2022) period the average ratio of PM2.5/PM10 was (0.319 ± 0.1640), showing a higher contribution to aerosol loading of traffic-related coarse particles in comparison with fine particles. The highest rates of the daily recorded COVID-19 incidence and death cases in Tokyo during the seventh COVID-19 wave (1 July 2022-1 October 2022) may be attributed to accumulation near the ground of high levels of air pollutants and viral pathogens due to: 1) peculiar persistent atmospheric anticyclonic circulation with strong positive anomalies of geopotential height at 500 hPa; 2) lower levels of Planetary Boundary Layer (PBL) heights; 3) high daily maximum air temperature and land surface temperature due to the prolonged heat waves (HWs) in summer 2022; 4) no imposed restrictions. Such findings can guide public decision-makers to design proper strategies to curb pandemics under persistent stable anticyclonic weather conditions and summer HWs in large metropolitan areas.

Potential ambient NO2 abatement by applying photocatalytic materials in a Spanish city and analysis of short-term effect on human mortality

Road traffic is the main contributor to NO₂ emissions in many European cities, causing that the current limit values for the protection of human health are exceeded. The use of photocatalytic compounds that incorporate titanium dioxide (TiO₂) is frequently proposed as abatement technology but its depolluting effectiveness on a real scale is still being investigated. In this work, the potential removal capacity of NO₂ that selected TiO₂-based materials would have if they were implemented in a street in the municipality of Alcobendas (Community of Madrid, Spain) has been evaluated. The number of avoided NO₂-related deaths over the locality across the period 2001-2019 have been inferred. Moreover, the saving associated with the estimated removal of ambient NO₂ due to the use of photocatalytic materials and costs generated by their acquisition and implementation in the selected urban environment were briefly studied. Attributable mortality due to NO₂ concentrations for Alcobendas has been estimated in 289 deaths, being 9241 the total deaths due to natural cause. This presents a monthly variation associated with the evolution of both mortality due to natural causes and the average concentrations of NO₂. The reduction in mortality via the hypothetical implantation of photocatalytic materials throughout the municipality, assuming ideal conditions for their optimal performance, would be a maximum of 3%. In addition, a saving of €5708 yr^-1 km^-2 related to NO_x damage costs of transport was obtained. A total cost of k€4750.5 km^-2 was associated to the purchase of photocatalytic materials and their application to all surfaces in that area. This technology has a big elimination potential in controlled conditions but a low reduction of ambient NO₂ is provided when implemented in real outdoor urban scenarios. Its use can be recommended incorporated into engineering designs and applications, complementing other abatement measures, to reduce NO₂ mortality in urban areas.

A machine learning-based study on the impact of COVID-19 on three kinds of pollution in Beijing-Tianjin-Hebei region

Large-scale restrictions on anthropogenic activities in China in 2020 due to the Corona Virus Disease 2019 (COVID-19) indirectly led to improvements in air quality. Previous studies have paid little attention to the changes in nitrogen dioxide (NO₂), fine particulate matter (PM_2.5) and ozone (O₃) concentrations at different levels of anthropogenic activity limitation and their interactions. In this study, machine learning models were used to simulate the concentrations of three pollutants during periods of different levels of lockdown, and compare them with observations during the same period. The results show that the difference between the simulated and observed values of NO₂ concentrations varies at different stages of the lockdown. Variation between simulated and observed O₃ and PM_2.5 concentrations were less distinct at different stages of lockdowns. During the most severe period of the lockdowns, NO₂ concentrations decreased significantly with a maximum decrease of 65.28 %, and O₃ concentrations increased with a maximum increase of 75.69 %. During the first two weeks of the lockdown, the titration reaction in the atmosphere was disrupted due to the rapid decrease in NO₂ concentrations, leading to the redistribution of Ox (NO₂ + O₃) in the atmosphere and eventually to the production of O₃ and secondary PM_2.5. The effect of traffic restrictions on the reduction of NO₂ concentrations is significant. However, it is also important to consider the increase in O₃ due to the constant volatile organic compounds (VOCs) and the decrease in NOx (NO+NO₂). Traffic restrictions had a limited effect on improving PM_2.5 pollution, so other beneficial measures were needed to sustainably reduce particulate matter pollution. Research on COVID-19 could provide new insights into future clean air action.

Impacts of emissions policies on future UK mortality burdens associated with air pollution

Air pollution is the greatest environmental risk to public health. Future air pollution concentrations are primarily determined by precursor emissions, which are driven by environmental policies relating to climate and air pollution. Detailed health impact assessments (HIA) are necessary to provide quantitative estimates of the impacts of future air pollution to support decision-makers developing environmental policy and targets. In this study we use high spatial resolution atmospheric chemistry modelling to simulate future air pollution concentrations across the UK for 2030, 2040 and 2050 based on current UK and European policy projections. We combine UK regional population-weighted concentrations with the latest epidemiological relationships to quantify mortality associated with changes in PM_2.5 and NO₂ air pollution. Our HIA suggests that by 2050, population-weighted exposure to PM_2.5 will reduce by 28% to 36%, and for NO₂ by 35% to 49%, depending on region. The HIA shows that for present day (2018), annual mortality attributable to the effects of long-term exposure to PM_2.5 and NO₂ is in the range 26,287 - 42,442, and that mortality burdens in future will be substantially reduced, being lower by 31%, 35%, and 37% in 2030, 2040 and 2050 respectively (relative to 2018) assuming no population changes. Including population projections (increases in all regions for 30+ years age group) slightly offsets these health benefits, resulting in reductions of 25%, 27%, and 26% in mortality burdens for 2030, 2040, 2050 respectively. Significant reductions in future mortality burdens are estimated and, importantly for public health, the majority of benefits are achieved early on in the future timeline simulated, though further efforts are likely needed to reduce impacts of air pollution to health.

Premature mortality attributable to NO2 exposure in cities and the role of built environment: A global analysis

BACKGROUND

Environmental risks accumulate in cities, including polluted air and health disparities, but these risks can be reduced through scientific city planning. The purpose of this study was to investigate the global burden of premature mortality attributable to NO₂ exposure in urban areas and the role of the built environment in this regard.

METHODS

An approach based on health impact assessment was used to estimate the premature mortality burdens associated with NO₂ exposure in 13,169 urban areas around the world using globally gridded NO₂ and population estimates, baseline mortality, and epidemiologically derived exposure-response functions. We used the most recent WHO recommended value (i.e.,10 μg/m³) as a counterfactual concentration. Finally, the relationship between the characteristics of the built environment at the city level and the burden of NO₂-related mortality was evaluated.

RESULTS

Worldwide, 549,715(95%CI: 276204-815,023) cases of death attributable to NO₂ exposure in urban areas could be prevented if compliance with the latest WHO guideline, accounting for 2.7 % (95%CI:1.4 %-4.0 %) of total mortalities in 2019. Across cities around the world, the age-standardized mortality rate (per 100,000 people) attributable to NO₂ exposure ranged from 51.3 (95%CI:25.8-76.0) in Central Asia to 3.4(95%CI: 1.7-5.1) in Oceania. Although there was a significant decrease in premature mortality attributable to NO₂ exposure globally, considerable regional heterogeneity exists, with cities in Central Asia and Andean Latin America in particular exhibiting an upward trend. Further, we discovered a positive association between population density and street connectivity with mortality attributable to NO₂. While the increase in green and blue space were significantly associated with a lower NO₂-associated mortality.

CONCLUSION

The findings of this study provided a comprehensive understanding of the premature mortality burden due to NO₂ in cities throughout the world and the role that urban planning policies can play in reducing the health burden associated with air pollution.

Climatology and landscape determinants of AOD, SO2 and NO2 over Indo-Gangetic Plain

Indo-Gangetic Plains (IGP) experiences high loading of particulate and gaseous pollutants all year around and is considered to be the most polluted regions of India. Understanding the effect of landscape determinants on air pollution in IGP regions is crucial to make its environment sustainable. We examined satellite retrievals of OMI NO₂ and SO₂, and MODIS AOD to analyse the long-term trend, spatio-seasonal pattern and dynamics of aerosols, NO₂ and SO₂ over three IGP regions, namely Upper Indo-Gangetic plain (UIGP), Middle Indo-Gangetic plain (MIGP) and Lower Indo-Gangetic plain (LIGP) over the period 2005-2019. IGP experienced an overall increment in AOD (R² = 0.63) and SO₂ (R² = 0.67) values, with LIGP (AOD, R² = 0.8 & SO₂, R² = 0.8) experiencing the largest rate of enhancement. The levels of NO₂ (R² = 0.2) experienced a decrement after 2012 (owing to implementation of vehicle emission policy) except in MIGP, with UIGP (R² = 0.23) exhibiting the largest rate of decrement. Seasonal heterogeneity in the nature of sources was observed over IGP regions. AOD (0.61 ± 0.1) and NO₂ value (3.82 ± 0.98 × 10¹⁵ molecules/cm²) were found highest during post-monsoon in UIGP owing to crop residue burning activity. The value of NO₂ (3.8 ± 1.4 × 10¹⁵ molecules/cm²) in MIGP was found highest during pre-monsoon due to high consumption of coal in power plants for summer cooling demand. The highest SO₂ level (0.09 ± 0.06 DU) was observed during post-monsoon in UIGP, as a large number of brick kilns are fired during this period. Correlations among landscape determinants and pollutants revealed that topography is the dominant variable that affect the spatial pattern of AOD compared to vegetation and land use. Lower elevation tends to have high AOD values compared to higher elevation. Vegetation-AOD relationship showed an inverse association in IGP regions and is influenced by factors such as seasonal meteorology and size of the airborne particles. Vegetation possesses positive relationship with SO₂ and NO₂, implying no pollution abatement effect on SO₂ and NO₂ pollutants. Built-up change has deteriorating effect as well as quenching effect on pollutants. Increase in built terrain have deteriorated the air quality in UIGP whereas it favored in suppressing the aerosol level in LIGP.

Long-term air pollution exposure and diabetes risk in American older adults: A national secondary data-based cohort study

Type 2 diabetes is a major public health concern. Several studies have found an increased diabetes risk associated with long-term air pollution exposure. However, most current studies are limited in their generalizability, exposure assessment, or the ability to differentiate incidence and prevalence cases. We assessed the association between air pollution and first documented diabetes occurrence in a national U.S. cohort of older adults to estimate diabetes risk. We included all Medicare enrollees 65 years and older in the fee-for-service program, part A and part B, in the contiguous United States (2000-2016). Participants were followed annually until the first recorded diabetes diagnosis, end of enrollment, or death (264, 869, 458 person-years). We obtained annual estimates of fine particulate matter (PM2.5), nitrogen dioxide (NO2), and warm-months ozone (O3) exposures from highly spatiotemporally resolved prediction models. We assessed the simultaneous effects of the pollutants on diabetes risk using survival analyses. We repeated the models in cohorts restricted to ZIP codes with air pollution levels not exceeding the national ambient air quality standards (NAAQS) during the study period. We identified 10, 024, 879 diabetes cases of 41, 780, 637 people (3.8% of person-years). The hazard ratio (HR) for first diabetes occurrence was 1.074 (95% CI 1.058; 1.089) for 5 μg/m3 increase in PM2.5, 1.055 (95% CI 1.050; 1.060) for 5 ppb increase in NO2, and 0.999 (95% CI 0.993; 1.004) for 5 ppb increase in O3. Both for NO2 and PM2.5 there was evidence of non-linear exposure-response curves with stronger associations at lower levels (NO2 ≤ 36 ppb, PM2.5 ≤ 8.2 μg/m3). Furthermore, associations remained in the restricted low-level cohorts. The O3-diabetes exposure-response relationship differed greatly between models and require further investigation. In conclusion, exposures to PM2.5 and NO2 are associated with increased diabetes risk, even when restricting the exposure to levels below the NAAQS set by the U.S. EPA.

Multi-output machine learning model for regional air pollution forecasting in Ho Chi Minh City, Vietnam

Air pollution concentrations in Ho Chi Minh City (HCMC) have been found to surpass the WHO standard, which has become a very serious problem affecting human health and the ecosystem. Various machine learning algorithms have recently been widely used in air quality forecasting studies to predict possible impacts. Training and constructing several machine learning models for different air pollutants, such as NO₂, SO₂, O₃, and CO forecasts, is a time-consuming process that necessitates additional effort for deployment, maintenance, and monitoring. In this paper, an effort has been made to develop a multi-step multi-output multivariate model (a global model) for air quality forecasting, taking into account various parameters such as meteorological conditions, air quality data from urban traffic, residential, and industrial areas, urban space information, and time component for the prediction of NO₂, SO₂, O₃, CO hourly (1 h to 24 h) concentrations. The global forecasting model can anticipate multiple air pollutant concentrations concurrently, based on past concentrations of covariate characteristics. The datasets on air pollution time series were gathered from six HealthyAir air quality monitoring sites in HCMC between February 2021 and August 2022. Darksky weather provided the hourly concentrations of meteorological conditions for the same period. This is the first model built using real-time air quality data for NO₂, SO₂, CO, and O₃ forecasting in HCM city. To assess the effectiveness of the proposed model, it was evaluated using real data from HealthyAir stations and quantified using Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and correlation indices. The results show that the global air quality forecasting model beats earlier models built for air quality forecasting of each specific pollutant in HCMC.

Long-term air pollution exposure and diabetes risk in American older adults: A national secondary data-based cohort study

Type 2 diabetes is a major public health concern. Several studies have found an increased diabetes risk associated with long-term air pollution exposure. However, most current studies are limited in their generalizability, exposure assessment, or the ability to differentiate incidence and prevalence cases. We assessed the association between air pollution and first documented diabetes occurrence in a national U.S. cohort of older adults to estimate diabetes risk. We included all Medicare enrollees 65 years and older in the fee-for-service program, part A and part B, in the contiguous United States (2000-2016). Participants were followed annually until the first recorded diabetes diagnosis, end of enrollment, or death (264, 869, 458 person-years). We obtained annual estimates of fine particulate matter (PM_2.5), nitrogen dioxide (NO₂), and warm-months ozone (O₃) exposures from highly spatiotemporally resolved prediction models. We assessed the simultaneous effects of the pollutants on diabetes risk using survival analyses. We repeated the models in cohorts restricted to ZIP codes with air pollution levels not exceeding the national ambient air quality standards (NAAQS) during the study period. We identified 10, 024, 879 diabetes cases of 41, 780, 637 people (3.8% of person-years). The hazard ratio (HR) for first diabetes occurrence was 1.074 (95% CI 1.058; 1.089) for 5 μg/m³ increase in PM_2.5, 1.055 (95% CI 1.050; 1.060) for 5 ppb increase in NO₂, and 0.999 (95% CI 0.993; 1.004) for 5 ppb increase in O₃. Both for NO₂ and PM_2.5 there was evidence of non-linear exposure-response curves with stronger associations at lower levels (NO₂ ≤ 36 ppb, PM_2.5 ≤ 8.2 μg/m³). Furthermore, associations remained in the restricted low-level cohorts. The O₃-diabetes exposure-response relationship differed greatly between models and require further investigation. In conclusion, exposures to PM_2.5 and NO₂ are associated with increased diabetes risk, even when restricting the exposure to levels below the NAAQS set by the U.S. EPA.

Stronger susceptibilities to air pollutants of influenza A than B were identified in subtropical Shenzhen, China

Air pollution was indicated to be a key factor contributing to the aggressive spread of influenza viruses, whereas uncertainty still exists regarding to whether distinctions exist between influenza subtypes. Our study quantified the impact of five air pollutants on influenza subtype outbreaks in Shenzhen, China, a densely populated and highly urbanized megacity. Daily influenza outbreak data of laboratory-confirmed positive cases were obtained from the Shenzhen CDC, from May 1, 2013 to Dec 31, 2015. Concentrations of nitrogen dioxide (NO₂), sulfur dioxide (SO₂), particulate matters ≤2.5 μm (PM_2.5), particulate matters ≤10 μm (PM₁₀), and ozone (O₃), were retrieved from the 18 national monitoring stations. The generalized additive model (GAM) and distributed lag non-linear model (DLNM) were used to calculate the concentration-response relationships between environmental inducers and outbreak epidemics, respectively for influenza A (Flu-A) and B (Flu-B). There were 1687 positive specimens were confirmed during the study period. The cold season was restricted from Nov. 4th to Apr. 20th, covering all seasons other than the long-lasting summer. Relatively heavy fine particle matter (PM_2.5) and NO₂ pollution was observed in cold months, with mean concentrations of 46.06 μg/m³ and 40.03 μg/m³, respectively. Time-series analysis indicated that high concentrations of NO₂, PM_2.5, PM₁₀, and O₃ were associated with more influenza outbreaks at short lag periods (0-5 d). Although more Flu-B (679 cases) epidemics occurred than Flu-A (382 cases) in the cold season, Flu-A generally showed higher susceptibility to air pollutants. A 10 μg/m³ increment in concentrations of PM_2.5, PM₁₀, and O₃ at lag 04, was associated with a 2.103 (95%CI: 1.528-2.893), 1.618 (95%CI: 1.311-1.996), and 1.569 (95%CI: 1.214-2.028) of the relative risk (RR) of Flu-A, respectively. A 5 μg/m³ increase in NO₂ was associated with higher risk of Flu-A at lag 03 (RR = 1.646, 95%CI: 1.295-2.092) and of Flu-B at lag 04 (RR = 1.319, 95%CI: 1.095-1.588). Nevertheless, barely significant effect of particulate matters (PM_2.5, PM₁₀) on Flu-B and SO₂ on both subtypes was detected. Further, the effect estimates of NO₂ increased for both subtypes when coexisting with other pollutants. This study provides evidence that declining concentrations of main pollutants including NO₂, O₃, and particulate matters, could substantially decrease influenza risk in subtropical Shenzhen, especially for influenza A.

Facile fabrication of nanoflower-like WO3/WS2 heterojunction for highly sensitive NO2 detection at room temperature

Realizing efficient detection of ultra-low concentrations of hazardous gases contributes to air pollution monitoring, ecosystem and human health protection. Herein, we firstly fabricated the nanoflower-like WO₃/WS₂ composites by a facile process to highly sensitively detect NO₂ at room temperature. The WO₃ content in the WO₃/WS₂ composites can be adjusted by altering the calcination temperature, and the WO₃ nanoparticles disperse uniformly on the WS₂ surface, forming the WO₃/WS₂ heterojunction. The room-temperature responses of WO₃/WS₂ composites gradually climb with the NO₂ concentration increasing from 0.005 to 5 ppm, and the WW-280 and WW-300 composites possess the optimal gas sensitivity when the NO₂ concentrations are lower and higher than 100 ppb, respectively. In particular, the two WO₃/WS₂ composites present the limitation of detection (LOD) of ≤ 5 ppb, and they exhibit the excellent selectivity, good reproducibility and long-term stability towards NO₂. A possible gas sensing mechanism was also proposed from the point of views of gas adsorption, redox reactions and electron transfer. The appropriate WO₃ content and molar ratio of hexagonal to monoclinic WO₃, and the formation of WO₃/WS₂ p-n heterojunction can contribute to the high sensitivity of WO₃/WS₂ composite to various concentrations of NO₂. This work offers a promising gas sensing material for room-temperature detection to low concentrations of NO₂.

Long-term air pollution exposure and COVID-19 case-severity: An analysis of individual-level data from Switzerland

Several studies are pointing out that exposure to elevated air pollutants could contribute to increased COVID-19 mortality. However, literature on the associations between air pollution exposure and COVID-19 severe morbidity is rather sparse. In addition, the majority of the studies used an ecological study design and were applied in regions with rather high air pollution levels. Here, we study the differential effects of long-term exposure to air pollution on severe morbidity and mortality risks from COVID-19 in various population subgroups in Switzerland, a country known for clean air. We perform individual-level analyses using data covering the first two major waves of COVID-19 between February 2020 and May 2021. High-resolution maps of particulate matter (PM_2.5) and nitrogen dioxide (NO₂) concentrations were produced for the 6 years preceding the pandemic using Bayesian geostatistical models. Air pollution exposure for each patient was measured by the long-term average concentration across the municipality of residence. The models were adjusted for the effects of individual characteristics, socio-economic, health-system, and climatic factors. The variables with an important association to COVID-19 case-severity were identified using Bayesian spatial variable selection. The results have shown that the individual-level characteristics are important factors related to COVID-19 morbidity and mortality in all the models. Long-term exposure to air pollution appears to influence the severity of the disease only when analyzing data during the first wave; this effect is attenuated upon adjustment for health-system related factors during the entire study period. Our findings suggest that the burden of air pollution increased the risks of COVID-19 in Switzerland during the first wave of the pandemic, but not during the second wave, when the national health system was better prepared.

Association of long-term exposure to ambient air pollutants with cardiac structure and cardiovascular function in Chinese adults

Epidemiological evidence increasingly suggests that air pollutants are intimately associated with the incidence and mortality of cardiovascular diseases (CVDs). However, studies on the association between chronic exposure to air pollutants and changes in left cardiac function and structure are limited. In our cross-sectional study, 3145 participants were enrolled from 6 provinces to explore the relationship between long-term air pollutants, cardiac structure, and cardiovascular function (e.g., blood lipids, blood pressure and pulse) in Chinese adults. Our study showed that exposure to five pollutants (NO₂, O₃, PM₁, PM_2.5 and PM₁₀) was associated with reduced left ventricular systolic function based on EF and SV parameters. These pollutants were also associated with increased pulses, where smaller particle sizes correlated significantly with pulses. Second, except for O₃, four pollutants were associated with decreased left ventricular diastolic parameters LVIDd and EDV and increased cardiac structural parameter IVSd. In addition, exposures to NO₂, O₃ and PM₁₀ were positively correlated with triglycerides in blood lipids. Overall, this study showed that chronic pollutant exposure is strongly associated with impaired left ventricular function in Chinese adults.

Impact of SARS-CoV-2 variants on mobility and air pollution in the United Kingdom

During the two years following the first case of COVID-19 in the United Kingdom, cycles of social restrictions were imposed to control the spread of the virus. These measures curtailed social contact and halted commercial and recreational activities affecting levels of air pollutants. As society adapted, restrictions eased and pollution gradually returned to baseline levels. However, resurgence in COVID-19 cases from new variants created a protracted and challenging path back to 'normality'. In this study, we retrospectively look back at the two years of COVID-19 and its prevalent variants, and examine the government response and its impact on mobility and air pollution. Results from a peak detection algorithm show peak events in mobility and COVID-19 deaths during variants periods decreased significantly from the wildtype COVID-19, despite the high contagiousness of these variants. Pollution levels remained below baseline with periods of significant increase for O₃, while NO₂ levels remained depleted, likely as a result of reduced traffic congestion as home office schemes have been maintained. Our findings suggest mobility and pollution return to baseline levels as immunity to COVID-19 increases.

Associations between long-term air pollution exposure and the incidence of cardiovascular diseases among American older adults

BACKGROUND & AIM

Numerous studies have linked air pollution with cardiovascular diseases. Fewer studies examined the associations at low concentration levels or assessed potential modifiers. Some investigations only examined hospitalizations, which can miss incident cases. This study aims to address these gaps through a nationwide cohort study of Medicare enrollees.

METHODS

Our study cohort comprise all Medicare enrollees (≥65 years old) continuously enrolled in the fee-for-service program and both Medicare part A and B across the contiguous U.S. from 2000 to 2016. We examined the associations of population-weighted ZIP code-level annual average PM_2.5, NO₂, and warm-season O₃ (May-October), with the first diagnoses of atrial fibrillation (AF), congestive heart failure (CHF), and stroke. We fit multi-pollutant Cox proportional hazards models adjusted for individual demographic characteristics and area-level covariates. We further examined these associations at low pollutant concentration levels and the potential effect modifications by race/ethnicity and comorbidities (diabetes, hypertension, hyperlipidemia).

RESULTS

Elevated PM_2.5 and NO₂ levels were associated with increased incidence of AF, CHF, and stroke. For each 1 μg/m³ increase in annual PM_2.5, hazard ratios (HRs) were 1.0059 (95%CI: 1.0054-1.0064), 1.0260 (95%CI: 1.0256-1.0264), and 1.0279 (95%CI: 1.0274-1.0284), respectively. For each1 ppb increase in annual NO₂, HRs are 1.0057 (95%CI: 1.0056-1.0059), 1.0112 (95%CI: 1.0110-1.0113), and 1.0095 (95%CI: 1.0093-1.0096), respectively. For warm-season O₃, each 1 ppb increase was associated with increased incidence of CHF (HR=1.0035, 95%CI: 1.0033-1.0037) and stroke (HR=1.0026, 95%CI: 1.0023-1.0028). Larger magnitudes of HRs were observed when restricted to pollutants levels lower than NAAQS standards. Generally higher risks were observed for Black people and diabetics.

CONCLUSIONS

Long-term exposure to PM_2.5, NO₂, and warm-season O₃ were associated with increased incidence of cardiovascular diseases, even at low pollutant concentration levels. Black people and people with diabetes were found to be vulnerable populations.

Long-term air pollution exposure, greenspace and health-related quality of life in the ECRHS study

BACKGROUND

Associations of long-term exposure to air pollution and greenspace with health-related quality of life (HRQOL) are poorly studied and few studies have accounted for asthma-rhinitis status.

OBJECTIVE

To assess the associations of air pollution and greenspace with HRQOL and whether asthma and/or rhinitis modify these associations.

METHODS

The study was based on the participants in the second (2000-2002, n = 6542) and third (2011-2013, n = 3686) waves of the European Community Respiratory Health Survey (ECRHS) including 19 centres. The mean follow-up time was 11.3 years. HRQOL was assessed by the SF-36 Physical and Mental Component Summary scores (PCS and MCS). NO₂, PM_2.5 and PM₁₀ annual concentrations were estimated at the residential address from existing land-use regression models. Greenspace around the residential address was estimated by the (i) mean of the Normalized Difference Vegetation Index (NDVI) and by the (ii) presence of green spaces within a 300 m buffer. Associations of each exposure variable with PCS and MCS were assessed by mixed linear regression models, accounting for the multicentre design and repeated data, and adjusting for potential confounders. Analyses were stratified by asthma-rhinitis status.

RESULTS

The mean (SD) age of the ECRHS-II and III participants was 43 (7.1) and 54 (7.2) years, respectively, and 48 % were men. Higher NO₂, PM_2.5 and PM₁₀ concentrations were associated with lower MCS (regression coefficients [95%CI] for one unit increase in the inter-quartile range of exposures were -0.69 [-1.23; -0.15], -1.79 [-2.88; -0.70], -1.80 [-2.98; -0.62] respectively). Higher NDVI and presence of forests were associated with higher MCS. No consistent associations were observed for PCS. Similar association patterns were observed regardless of asthma-rhinitis status.

CONCLUSION

European adults who resided at places with higher air pollution and lower greenspace were more likely to have lower mental component of HRQOL. Asthma or rhinitis status did not modify these associations.

Marginal reduction in surface NO2 attributable to airport shutdown: A machine learning regression-based approach

Emissions from aviation and airport-related activities degrade surface air quality but received limited attention relative to regular transportation sectors like road traffic and waterborne vessels. Statistically, assessing the impact of airport-related emissions remains a challenge due to the fact that its signal in the air quality time series data is largely dwarfed by meteorology and other emissions. Flight-ban policy has been implemented in a number of cities in response to the COVID-19 spread since early 2020, which provides an unprecedented opportunity to examine the changes in air quality attributable to airport closure. It would also be interesting to know whether such an intervention produces extra marginal air quality benefits, in addition to road traffic. Here we investigated the impact of airport-related emissions from a civil airport on nearby NO₂ air quality by applying machine learning predictive model to observational data collected from this unique quasi-natural experiment. The whole lockdown-attributable change in NO₂ was 16.7 μg/m³, equals to a drop of 73% in NO₂ with respect to the business-as-usual level. Meanwhile, the airport flight-ban aviation-attributable NO₂ was 3.1 μg/m³, accounting for a marginal reduction of 18.6% of the overall NO₂ change that driven by the whole lockdown effect. The airport-related emissions contributed up to 24% of the local ambient NO₂ under normal conditions. Additionally, the average impact of airport-related emissions on the nearby air quality was ∼0.01 ± 0.001 μg/m³ NO₂ per air-flight. Our results highlight that attention needs to be paid to such a considerable emission source in many places where regular air quality regulatory measures were insufficient to bring NO₂ concentration into compliance with the health-based limit.

Time series of transcriptome analysis in entire lung development stages provide insights into the origin of NO2 related lung diseases

Lung growth is a critical window, when exposure to various pollutants can disturb the finely-tuned lung development and enhance risk of long-term structural and functional sequelae of lung. In this study, pregnant C57/6 mice were treated with NO2, and lungs of fetus/offspring were collected at different developmental windows and dynamic lung development was determined. The results showed that maternal NO2 exposure suppressed fetal weight, implying that fetal development can be disturbed. The time-series RNA-seq analysis of lungs showed that maternal NO2 exposure induced significant time-dependent changes in the expression profiles of genes associated with lung vein myocardium development in fetus/offspring. Most of these genes in NO2 exposure group were suppressed at middle gestation and at birth. Our results also indicated that the gene expressions of Nkx2.5 in NO2 exposure were suppressed to 0.27- and 0.44-fold of the corresponding Air group at E13.5 and PND1, and restored at later time points. This indicated that the transcription factor Nkx2.5 played an important role in abnormal lung development in fetus/offspring caused by maternal NO2 exposure. Importantly, gene expressions of lung vein myocardium development were related to transcription factors (TFs) and lung functions, and TFs showed similar trends with lung function. These results provide a comprehensive view of the adverse effects of maternal NO2 exposure on fetal lung development by uncovering molecular targets and related signaling pathways at the transcriptional level.

Associations of air pollution with COVID-19 positivity, hospitalisations, and mortality: Observational evidence from UK Biobank

Individual-level studies with adjustment for important COVID-19 risk factors suggest positive associations of long-term air pollution exposure (particulate matter and nitrogen dioxide) with COVID-19 infection, hospitalisations and mortality. The evidence, however, remains limited and mechanisms unclear. We aimed to investigate these associations within UK Biobank, and to examine the role of underlying chronic disease as a potential mechanism. UK Biobank COVID-19 positive laboratory test results were ascertained via Public Health England and general practitioner record linkage, COVID-19 hospitalisations via Hospital Episode Statistics, and COVID-19 mortality via Office for National Statistics mortality records from March-December 2020. We used annual average outdoor air pollution modelled at 2010 residential addresses of UK Biobank participants who resided in England (n = 424,721). We obtained important COVID-19 risk factors from baseline UK Biobank questionnaire responses (2006-2010) and general practitioner record linkage. We used logistic regression models to assess associations of air pollution with COVID-19 outcomes, adjusted for relevant confounders, and conducted sensitivity analyses. We found positive associations of fine particulate matter (PM_2.5) and nitrogen dioxide (NO₂) with COVID-19 positive test result after adjustment for confounders and COVID-19 risk factors, with odds ratios of 1.05 (95% confidence intervals (CI) = 1.02, 1.08), and 1.05 (95% CI = 1.01, 1.08), respectively. PM 2.5 and NO 2 were positively associated with COVID-19 hospitalisations and deaths in minimally adjusted models, but not in fully adjusted models. No associations for PM₁₀ were found. In analyses with additional adjustment for pre-existing chronic disease, effect estimates were not substantially attenuated, indicating that underlying chronic disease may not fully explain associations. We found some evidence that long-term exposure to PM_2.5 and NO₂ was associated with a COVID-19 positive test result in UK Biobank, though not with COVID-19 hospitalisations or deaths.

Long-term characterization of roadside air pollutants in urban Beijing and associated public health implications

Road traffic constitutes a major source of air pollutants in urban Beijing, which are responsible for substantial premature mortality. A series of policies and regulations has led to appreciable traffic emission reductions in recent decades. To shed light on long-term (2014-2020) roadside air pollution and assess the efficacy of traffic control measures and their effects on public health, this study quantitatively evaluated changes in the concentrations of six key air pollutants (PM_2.5, PM₁₀, NO₂, SO₂, CO and O₃) measured at 5 roadside and 12 urban background monitoring stations in Beijing. We found that the annual mean concentrations of these air pollutants were remarkably reduced by 47%-71% from 2014 to 2020, while the concurrent ozone concentration increased by 17.4%. In addition, we observed reductions in the roadside increments in PM_2.5, NO₂, SO₂ and CO of 54.8%, 29.8%, 20.6%, and 59.1%, respectively, indicating the high effectiveness of new vehicle standard (China V and VI) implementation in Beijing. The premature deaths due to traffic emissions were estimated to be 8379 and 1908 cases in 2014 and 2020, respectively. The impact of NO₂ from road traffic relative to PM_2.5 on premature mortality was comparable to that of traffic-related PM_2.5 emissions. The public health effect of SO₂ originating from traffic was markedly lower than that of PM_2.5. The results indicated that a reduction in traffic-related NO₂ could likely yield the greatest benefits for public health.

Light-assisted room temperature gas sensing performance and mechanism of direct Z-scheme MoS2/SnO2 crystal faceted heterojunctions

Light assistance and construction of heterojunctions are both promising means to improve the room temperature gas sensing performance of MoS₂ recently. However, enhancing the separation efficiency of photo-generated carriers at interface and adsorption ability of surface have become the bottleneck problem to further improve the room temperature gas sensing performance of MoS₂-based heterojunctions under light assistance. In the present study, a novel direct Z-scheme MoS_2/SnO₂ heterojunction was designed through crystal facets engineering and its room temperature gas sensing properties under light assistance was studied. It was found that the heterojunction showed outstanding room temperature NO₂ sensing performance with a high response of 208.66 toward 10 ppm NO_2, together with excellent recovery characteristics and selectivity. The gas sensing mechanism study suggested that high-energy {221} crystal facets of SnO₂ and MoS₂ directly formed Z-scheme heterojunction, which could greatly improve the separation efficiency of photo-generated carriers with high redox capacity. Moreover, {221} facets greatly enhanced adsorption ability towards NO₂. This work not only opens up the application of Z-scheme heterojunctions in gas sensing, which will greatly promotes the development of room temperature light-assisted gas sensors, but also provides a new idea for the construction of direct Z-scheme heterojunctions through crystal facets engineering.

Childhood cancer and traffic-related air pollution in Switzerland: A nationwide census-based cohort study

Motor vehicle exhaust is a major contributor to air pollution, and exposure to benzene or other carcinogenic components may increase cancer risks. We aimed to investigate the association between traffic-related air pollution and risk of childhood cancer in a nationwide cohort study in Switzerland. We identified incident cases from the Swiss Childhood Cancer Registry diagnosed < 16 years of age between 1990 and 2015 and linked them probabilistically with the census-based Swiss National Cohort study. We developed land use regression models to estimate annual mean ambient levels of nitrogen dioxide (NO2) and benzene outside 1.4 million children's homes. We used risk-set sampling to facilitate the analysis of time-varying exposure and fitted conditional logistic regression models adjusting for neighborhood socio-economic position, level of urbanization, and background ionizing radiation. We included 2,960 cancer cases in the analyses. The adjusted hazard ratios (HR) and 95% confidence intervals for exposure to NO2 per 10 μg/m3 were 1.00 (95%-CI 0.88-1.13) for acute lymphoblastic leukemia (ALL) and 1.31 (95%-CI 1.00-1.71) for acute myeloid leukemia (AML). Using exposure lagged by 1 to 5 years instead of current exposure attenuated the effect for AML. The adjusted HR for exposure to benzene per 1 μg/m3 was 1.03 (95%-CI 0.86-1.23) for ALL and 1.29 (95%-CI 0.86-1.95) for AML. We also observed increased HRs for other diagnostic groups, notably non-Hodgkin lymphoma. Our study adds to the existing evidence that exposure to traffic-related air pollution is associated with an increased risk of childhood leukemia, particularly AML.

Source analysis of the tropospheric NO2 based on MAX-DOAS measurements in northeastern China

Ground-based Multi-Axis Differential Optical Absorption Spectroscopy (Max-DOAS) measurements of nitrogen dioxide (NO₂) were continuously obtained from January to November 2019 in northeastern China (NEC). Seasonal variations in the mean NO₂ vertical column densities (VCDs) were apparent, with a maximum of 2.9 × 10¹⁶ molecules cm^-2 in the winter due to enhanced NO₂ emissions from coal-fired winter heating, a longer photochemical lifetime and atmospheric transport. Daily maximum and minimum NO₂ VCDs were observed, independent of the season, at around 11:00 and 13:00 local time, respectively, and the most obvious increases and decreases occurred in the winter and autumn, respectively. The mean diurnal NO₂ VCDs at 11:00 increased to at 08:00 by 1.6, 5.8, and 6.7 × 10¹⁵ molecules cm^-2 in the summer, autumn and winter, respectively, due to increased NO₂ emissions, and then decreased by 2.8, 4.2, and 5.1 × 10¹⁵ molecules cm^-2 at 13:00 in the spring, summer, and autumn, respectively. This was due to strong solar radiation and increased planetary boundary layer height. There was no obvious weekend effect, and the NO₂ VCDs only decreased by about 10% on the weekends. We evaluated the contributions of emissions and transport in the different seasons to the NO₂ VCDs using a generalized additive model, where the contributions of local emissions to the total in the spring, summer, autumn, and winter were 89 ± 12%, 92 ± 11%, 86 ± 12%, and 72 ± 16%, respectively. The contribution of regional transport reached 26% in the winter, and this high contribution value was mainly correlated with the northeast wind, which was due to the transport channel of air pollutants along the Changbai Mountains in NEC. The NO₂/SO₂ ratio was used to identify NO₂ from industrial sources and vehicle exhaust. The contribution of industrial NO₂ VCD sources was >66.3 ± 16% in Shenyang due to the large amount of coal combustion from heavy industrial activity, which emitted large amounts of NO₂. Our results suggest that air quality management in Shenyang should consider reductions in local NO₂ emissions from industrial sources along with regional cooperative control.

Trends and characteristics of ozone and nitrogen dioxide related health impacts in Chinese cities

Ambient ozone pollution has been becoming severe and attributed to considerable health impacts in China. Nitrogen dioxide (NO₂) is involved in atmospheric ozone production while also affecting public health directly. Joint control ozone and NO₂ pollution would be of significance. This study quantitatively assessed the health impact attributed to ambient ozone and NO₂ pollution in 338 Chinese cities from 2015 to 2020. The results reveal the generally opposite trends of ozone- and NO₂-related health impacts in China. From 2015-2020, respiratory and chronic obstructive pulmonary disease (COPD) health impacts attributed to ozone in 338 cities increased by 65.30% and 63.98%. The NO₂-attributed health impacts decreased by 24.80% and 24.62%. In 2020, the ozone- and NO₂-related respiratory health impacts were 3.96 million DALYs (disability-adjusted life years) and 1.47 million DALYs. High health impacts are concentrated in big cities and city clusters. In 2020, the sum of ozone- and NO₂-related respiratory health impacts in the top 20 cities was 0.98 million DALYs and 0.44 million DALYs, accounting for 24.70% and 30.24% of the 338 cities. The population attribution fraction analysis identified the increasing distributional consistency of ozone and NO₂-related health impacts, emphasizing the necessity and possible efficiency of ozone-NO₂ joint control. Emission source analysis based on gridded data provided a reference for understanding health impacts and developing targeted strategies.

A unified empirical modeling approach for particulate matter and NO2 in a coastal city in China

Modeling air pollutants on a fine spatiotemporal scale is necessary for health studies that focus on critical short-term exposure windows. A unified empirical modeling approach is useful for health studies; however, it is unclear whether this approach can be used in a coastal city for air pollutants driven by local emissions and regional meteorological factors. An advanced empirical modeling approach was used to develop exposure models from October 2012 to December 2019, for particulate matter with aerodynamic diameters less than or equal to 2.5 and 10 μm (PM_2.5 and PM₁₀) and nitrogen dioxide (NO₂) in the coastal city of Shanghai, China. Air pollutant concentrations were obtained from daily measurements at 55 administrative monitoring sites that were integrated into three-day average concentrations. Data on a large array of geographic variables were collected, and their dimensions were reduced using the partial least squares regression method. A geostatistical model using the land-use regression approach in a universal kriging framework was developed to estimate short-term exposure concentrations. The prediction ability of the models were determined by leave-one (site)-out cross-validation (LOOCV) and external validation (EV). Compared to the LOOCV results, the EV results for PM_2.5 and PM₁₀ were consistently reliable, but the EV for NO₂ had a larger root mean squared error. The temporal random effects involved in the model structure were interpreted using sensitivity analyses. This affected the short-term PM_2.5 and PM₁₀ model predictions. This unified empirical modeling approach was successfully used for particulate matter in Shanghai, where air pollution is affected by complex regional and meteorological conditions. These exposure models are going to be applied for making exposure predictions at residential locations for short-term exposure predictions in the "Growth trajectories and air pollution" (GAAP) study in Shanghai that focuses on maternal and early life exposure to air pollutants.