Annual Average NO2 Concentrations Research Articles

Risk analysis of air pollutants and types of anemia: a UK Biobank prospective cohort study.

Previous studies have suggested that exposure to air pollutants may be associated with specific blood indicators or anemia in certain populations. However, there is insufficient epidemiological data and prospective evidence to evaluate the relationship between environmental air pollution and specific types of anemia. We conducted a large-scale prospective cohort study based on the UK Biobank. Annual average concentrations of NO2, PM2.5, PM2.5-10, and PM10 were obtained from the ESCAPE study using the Land Use Regression (LUR) model. The association between atmospheric pollutants and different types of anemia was investigated using the Cox proportional hazards model. Furthermore, restricted cubic splines were used to explore exposure-response relationships for positive associations, followed by stratification and effect modification analyses by gender and age. After adjusting for demographic characteristics, 3-4 of the four types of air pollution were significantly associated with an increased risk of iron deficiency, vitamin B12 deficiency and folate deficiency anemia, while there was no significant association with other defined types of anemia. After full adjustment, we estimated that the hazard ratios (HRs) of iron deficiency anemia associated with each 10 μg/m3 increase in NO2, PM2.5, and PM10 were 1.04 (95%CI: 1.02, 1.07), 2.00 (95%CI: 1.71, 2.33), and 1.10 (95%CI: 1.02, 1.20) respectively. The HRs of folate deficiency anemia with each 10 μg/m3 increase in NO2, PM2.5, PM2.5-10, and PM10 were 1.25 (95%CI: 1.12, 1.40), 4.61 (95%CI: 2.03, 10.47), 2.81 (95%CI: 1.11, 7.08), and 1.99 (95%CI: 1.25, 3.15) respectively. For vitamin B12 deficiency anemia, no significant association with atmospheric pollution was found. Additionally, we estimated almost linear exposure-response curves between air pollution and anemia, and interaction analyses suggested that gender and age did not modify the association between air pollution and anemia. Our research provided reliable evidence for the association between long-term exposure to PM10, PM2.5, PM2.5-10, NO2, and several types of anemia. NO2, PM2.5, and PM10 significantly increased the risk of iron deficiency anemia and folate deficiency anemia. Additionally, we found that the smaller the PM diameter, the higher the risk, and folate deficiency anemia was more susceptible to air pollution than iron deficiency anemia. No association was observed between the four types of air pollution and hemolytic anemia, aplastic anemia, and other types of anemia. Although the mechanisms are not well understood, we emphasize the need to limit the levels of PM and NO2 in the environment to reduce the potential impact of air pollution on folate and iron deficiency anemia.

The variability of NO2 concentrations over China based on satellite and influencing factors analysis during 2019–2021

The variation of tropospheric nitrogen dioxide (NO2) vertical column densities (VCDs) indirectly reflects the difference in pollution emissions from industrial production and transportation. Accurately analyzing its pollution sources and driving factors plays an important role in energy conservation, emission reduction, and air pollution reduction. NO2 concentration products of Sentinel-5P (Sentinel-5 Precursor) TROPOMI (TROPOspheric Monitoring Instrument) from 2019 to 2021 and Aura OMI (Ozone Monitoring Instrument) from 2009 to 2021, combined with China’s main energy consumption, the growth value of the industry, Gross Domestic Product (GDP), and other data were used to analyze the influencing factors of NO2 variations. Firstly, NO2 tropospheric vertical column densities (NO2 TVCDs) of China increased by 14.72% and 3.26% in 2021 and 2020 compared with the 2019. The secondary and tertiary industry and the national energy consumption increased synchronously, which was highly related to the increase in NO2 TVCDs. Secondly, the impact of COVID-19 (coronavirus disease 2019) on China’s industrial production and residents was mainly concentrated in the first quarter of 2020, which leading to a decline in the annual average NO2 concentration in densely populated areas in 2020 compared to the same period in 2019. The industrial production scale and production capacity has gradually recovered since April 2020, and the NO2 concentration has gradually reached or exceeded the level of the same period of 2019. Finally, atmospheric pollution prevention and control measures played a positive role in the decline of NO2 of China.

Urban–rural disparities in the association of nitrogen dioxide exposure with cardiovascular disease risk in China: effect size and economic burden

BackgroundTogether with rapid urbanization, ambient nitrogen dioxide (NO2) exposure has become a growing health threat. However, little is known about the urban–rural disparities in the health implications of short-term NO2 exposure. This study aimed to compare the association between short-term NO2 exposure and hospitalization for cardiovascular disease (CVD) among urban and rural residents in Shandong Province, China. Then, this study further explored the urban–rural disparities in the economic burden attributed to NO2 and the explanation for the disparities.MethodsDaily hospitalization data were obtained from an electronic medical records dataset covering a population of 5 million. In total, 303,217 hospital admissions for CVD were analyzed. A three-stage time-series analytic approach was used to estimate the county-level association and the attributed economic burden.ResultsFor every 10-μg/m3 increase in NO2 concentrations, this study observed a significant percentage increase in hospital admissions on the day of exposure of 1.42% (95% CI 0.92 to 1.92%) for CVD. The effect size was slightly higher in urban areas, while the urban–rural difference was not significant. However, a more pronounced displacement phenomenon was found in rural areas, and the economic burden attributed to NO2 was significantly higher in urban areas. At an annual average NO2 concentration of 10 μg/m3, total hospital days and expenses in urban areas were reduced by 81,801 (44,831 to 118,191) days and 60,121 (33,002 to 86,729) thousand CNY, respectively, almost twice as much as in rural areas. Due to disadvantages in socioeconomic status and medical resources, despite similar air pollution levels in the urban and rural areas of our sample sites, the rural population tended to spend less on hospitalization services.ConclusionsShort-term exposure to ambient NO2 could lead to considerable health impacts in either urban or rural areas of Shandong Province, China. Moreover, urban–rural differences in socioeconomic status and medical resources contributed to the urban–rural disparities in the economic burden attributed to NO2 exposure. The health implications of NO2 exposure are a social problem in addition to an environmental problem. Thus, this study suggests a coordinated intervention system that targets environmental and social inequality factors simultaneously.

PM10 increases mortality risk in rheumatoid arthritis-associated interstitial lung disease

ObjectivesThe effect of air pollution on the prognosis of rheumatoid arthritis-associated interstitial lung disease (RA-ILD) remains poorly understood. We aimed to evaluate the effect of long-term exposure to particulate matter...

Quantifying urban heat island and pollutant nexus: A novel geospatial approach

The interaction between Urban Heat Island (UHI) effects and air pollution in urban areas significantly impacts both the well-being of inhabitants and the delicate balance of urban ecology and climate. This study investigates the interactions between UHI effects and pollutants (CO, HCHO, aerosols, NO2, O3, and SO2) distribution in Hyderabad over four years (2019–2022) within spatial and thermal interfaces. The study identified that there is a significantly higher concentration of pollutants in the urban areas compared to the rural areas. In urban areas, the UHI indicator shows a strong positive correlation with pollutants like CO, NO2, and O3, a weaker positive correlation with HCHO, and aerosols, and a weak negative correlation with SO2. A comprehensive framework to analyse the combined impact of pollutants, yielding insights into cumulative atmospheric pollution levels - UPI indicator is proposed in this study. The subsequent generation of pollution risk map identified that around 80 % of the urban expanse is highly contaminated zones. The conjunction of thermal hotspots and high contamination zones highlights nuanced UHI-pollution associations, unveiling high-risk zones with elevated temperatures and pollutants. The high-risk zones exhibit elevated annual average concentrations of NO2, aerosols, HCHO, and CO, surpassing the non-high-risk zone by 53.535 %, 21.707 %, 7.561 %, and 1.194 %, respectively. Additionally, the high-risk zones experience a temperature higher than the non-high-risk zone by 2.601 °C. This research informs urban planning and policies, contributing to a holistic understanding of UHI-pollution dynamics and their implications.

Cu-MOF-74-derived Cu2O/CuO-C nanocomposite as an effective chemiresistive sensor for detection of NO2 at room temperature

We report a Cu2O/CuO-C nanocomposite which fabricated by thermal decomposition of Cu-based metal-organic framework (Cu-MOF-74) precursor. The attractive advantages of MOF-74 incorporating the abundant unsaturated (open) metal sites and excellent stability provide a guarantee for developing of efficient gas-sensing materials. After annealing, the material retains its skeleton structure and the surface area is greatly increased. The high porosity feature of the as-prepared Cu2O/CuO-C offers plentiful active sites for adsorption of gas molecules, thus ensuring high sensing performance. The results indicate that the Cu2O/CuO-C-based sensor exhibits high sensitivity (1.03–30 ppb NO2), good linear relationship and excellent selectivity to NO2 at room temperature (RT, 25 ℃). Notably, the theoretical detection limit of the as-prepared sensor is ∼7 ppb, which is significantly below the annual average NO2 concentration limit (40 ppm) imposed by China. The synergistic effect between CuO/Cu2O heterostructure and carbon, and the derived solid structure are considered as the main reason for high sensitivity and selectivity. In addition, the high oxygen vacancy concentration is also the key to ensure excellent gas sensitivity. This work provides a new choice for developing high performance room temperature NO2 gas sensor.

Densification versus urban sprawl. Modeling the impact of two urban growth scenarios on air quality

This study analyzes the impact of urban form on air pollution for two urban growth scenarios in 2040 for Quito, Ecuador: Urban sprawl (low-density outward expansion) and densification (the population increase occurs within the 2017 city boundaries). We estimate concentrations of carbon monoxide (CO), nitrogen oxides (NOx), nitrogen dioxide (NO2), ozone (O3), and particulate matter with an aerodynamic diameter equal to or less than 2.5 μm (PM2.5). Two downscaled global emission pathways (ECLIPSE) that present a lower/upper range are applied: 1) Current legislation emissions, CLE, and 2) Maximum technically feasible reductions, MTFR. The population is expected to increase by 50%, from 3 million in 2017 to 4.5 million in 2040. The sprawl scenario considerably increases the urban area by 52%. However, these new areas hold only 9% of the population. Both pathways present considerably lower emissions for 2040 than in 2017. Emissions for all sectors, except transportation, are the same for both scenarios. Transport emissions for the densification scenario are 32% lower than for urban sprawl.Due to reduced emissions, the 2040 scenarios result in lower CO, NOx, and PM2.5 concentrations for both pathways compared to 2017. This drop is more marked for the MTFR pathway compared to CLE. Contrary to our expectations, the two scenarios result in similar annual concentrations at the metropolitan level. Even when emission variations are considerable, differences between the scenarios are relatively small, particularly for CO, O3, and PM2.5, due to high regional contributions. However, the spatial distribution of concentrations within urban areas differs significantly; thus, the sprawl scenario results in a larger surface with higher CO, NOx, NO2, and PM2.5 levels and the opposite for O3. Still, due to the population distribution, the densification scenario exposes more residents to higher CO, O3, and PM2.5 values, and the sprawl scenario exposes more inhabitants to higher concentrations of NO2 and NOx.We find an improvement in air quality for both scenarios in 2040 compared to 2017. The annual average NO2 concentrations exceed local regulations in a small urban area only for the CLE pathway. In contrast, for PM2.5, the area and population exposed to levels that exceed the local regulation are higher than that for NO2 and more significant for the densification than the sprawl scenario and for the CLE than the MTFR pathway.

An Analytical Study on Air Quality of Some Historical and Cultural Cities in Uttar Pradesh (India) with Special Reference to the Environment and Human Health

Abstract: The rapid urbanization, different construction works and deforestation recently increased in some famous historical, cultural and religious cities in Uttar Pradesh due to higher population pressure. Thousands of pilgrims visit these cities every year by motor and other vehicles. The motor vehicle exhaust also plays a vital role to enhance the toxic gases and aerosol in the environment. Many toxic particles, molecules, toxic gasses, chemicals, dust particles, vehicle exhausts and microorganisms also help to increase deterioration in air quality. Some factors like particulate matter, sulphur di oxide, nitrogen oxide, carbon mono oxide, carbon di oxide in higher concentrations directly affect and change the air texture as well as air quality. Higher concentrations of these substances are very injurious to the environment as well as living being including human. In present investigation a comparative study is done in between the residential and industrial/commercial area of five famous historical, cultural religious cities of Uttar Pradesh i.e. Varanasi, Prayagraj, Mathura, Gorakhpur and Ayodhya. Four air quality parameters i.e., air quality index (AQI), SO2, NO2 and PM10 average annual concentrations have been taken for this analytical study. AQI (Air quality index) was found 2 to 4 times higher in residential area while 3-6 times higher in industrial/commercial area in comparison to normal AQI value (0-50) in all studied cities/area. The highest average annual AQI value was recorded (166.40 microgram/m3) in Saket Nagar residential of Varanasi while 313.36 recorded in Geeda industrial area of Gorakhpur. The annual average of sulphur dioxide (SO2) concentration found 26.46 (microgram/m3) and 26.12 (microgram/m3) in residential area Varanasi and Prayagraj respectively which is slightly higher than the standard value (20microgram/m3). SO2 concentration recorded higher almost in industrial area of all cities. It was found highest in Geeda industrial area of Gorakhpur (49.70 microgram/m3) in comparison to normal annual value (20 microgram/m3). The annual average NO2 concentration in residential area almost found below or equal to normal value but it was recorded more than 3 times (33.44 microgram/m3) in Geeda industrial area of Gorakhpur. The particulate matter (PM10) concentration recorded very high in all residential as well as industrial/commercial area of all studied cities. The highest PM10 concentration 198.47(microgram/m3) found in Saket Nagar residential area of Varanasi city which is about 20 times higher than the normal value (10 microgram/m3). The highest average annual concentration of PM10 recorded 355.06 (microgram/m3) in Geeda industrial area of Gorakhpur which is about 36 times higher than normal annual value. The increasing concentration of the studied factors adversely affecting the environment as well as human health in these cities. The long term or short-term exposure of these pollutants is responsible for increasing various pulmonary disease in these cities.

Change in Air Quality during 2014-2021 in Jinan City in China and Its Influencing Factors.

Air pollution affects climate change, food production, traffic safety, and human health. In this paper, we analyze the changes in air quality index (AQI) and concentrations of six air pollutants in Jinan during 2014-2021. The results indicate that the annual average concentrations of PM10, PM2.5, NO2, SO2, CO, and O3 and AQI values all declined year after year during 2014-2021. Compared with 2014, AQI in Jinan City fell by 27.3% in 2021. Air quality in the four seasons of 2021 was obviously better than that in 2014. PM2.5 concentration was the highest in winter and PM2.5 concentration was the lowest in summer, while it was the opposite for O3 concentration. AQI in Jinan during the COVID epoch in 2020 was remarkably lower compared with that during the same epoch in 2021. Nevertheless, air quality during the post-COVID epoch in 2020 conspicuously deteriorated compared with that in 2021. Socioeconomic elements were the main reasons for the changes in air quality. AQI in Jinan was majorly influenced by energy consumption per 10,000-yuan GDP (ECPGDP), SO2 emissions (SDE), NOx emissions (NOE), particulate emissions (PE), PM2.5, and PM10. Clean policies in Jinan City played a key role in improving air quality. Unfavorable meteorological conditions led to heavy pollution weather in the winter. These results could provide a scientific reference for the control of air pollution in Jinan City.

Analysis of The Effect of Deforestation Rates on Air Pollution Concentration and Land Surface Temperature Using Landsat-8 Imagery with Google Earth Engine (Case Study: East Kalimantan Province, 2019-2020)

The increase in population triggers an increase in deforestation rates. According to data from Ministry of Environment and Forestry, East Kalimantan’s highest deforestation rate reached 54.8 thousand hectares in 2018-2019. This study analyzed the effect of deforestation rates on air pollutant concentrations (SO2 and NO2) and land surface temperature (LST) in East Kalimantan in 2019-2020 with Google Earth Engine (GEE) platform using Landsat 8 Level 2 surface reflectance imagery. The TIR channel was converted into brightness temperature which further used as the input in LST estimation using Mono-Window Algorithm, SO2 estimation using Hasan Algorithm (2014), and NO2 estimation using Alseroury Algorithm (2015). During 2019-2020, the average LST value was 24.2 °C, which was increased by 2.0 °C from the previous year. The maximum NO2 concentration was 189.771 g/m3. The annual average NO2 concentration increased by 0.757 g/m³ from the previous year. The maximum concentration of SO2 was 0.521 ppm. The annual average SO2 concentration increased by 0.011 ppm from the previous year. To determine the effect of deforestation rates on the concentration of SO2, NO2 and LST, the Pearson correlation test was carried out. The correlation coefficient between deforestation rates and SO2 is R = 0.949 (very strong correlation); correlation coefficient between deforestation rates and NO2 is R = 0.889 (very strong correlation); and the correlation coefficient between the deforestation rates and the LST is R = 0.887 (very strong correlation). This shows that the higher deforestation rates, will caused in the higher concentration of SO2, NO2, and LST.

Association of NO2 with daily hospital admissions for mental disorders: Investigation of the modification effects of green spaces and long-term NO2 exposure

Air pollution is a risk factor for increased hospital admissions due to mental disorders, while green spaces have been linked with better mental health. We linked daily hospital admission records from Wuhan's 74 municipal hospitals from 2017 to 2019 with modeled annual average NO2 concentrations and added data on the residential surrounding green spaces with 250 m and 500 m buffers based on the normalized difference vegetation index (NDVI) using a land use regression model (LUR). The conditional logistic regression model was used to estimate the acute effect of short-term NO2 exposure, and stratification analyses were applied to explore the modification effect of long-term NO2 exposure and green spaces by estimating the odds ratios in the single- and dual-environmental factor groups. A total of 42,705 hospital admissions for mental disorders were identified. Short-term exposure to NO2 was associated with an increased risk of hospital admission for mental disorders. A 10 μg/m3 increase in NO2 (lag01 day) was associated with an increase in hospital admissions of 2.86% (95% CI, 2.05–3.68) for the total mental disorders. Compared with patients in the “low-NDVI/low-NO2” group (ER = 2.27%, 95% CI, 0.27–4.31), patients in the “high-NDVI/low-NO2” group (ER = 1.93%, −0.10−3.99) showed a lower and insignificant increase in hospitalizations for the total mental disorders, while greenness had a slight moderating effect in the high-level long-term NO2 exposure areas. This study suggested that green spaces may moderate the acute effect of NO2 exposure for mental disorder hospitalizations, especially in low-level long-term NO2 exposure areas.

Associations between ambient air pollution, obesity, and serum vitamin D status in the general population of Korean adults

BackgroundAlthough a growing body of evidence suggests air pollution is associated with low serum vitamin D status, few studies have reported whether obesity status affects this relationship. The aim of this study was to identify associations between ambient air pollution exposure, obesity, and serum vitamin D status in the general population of South Korea.MethodsThis study was conducted in a cross-sectional design. A total of 30,242 Korean adults from a nationwide general population survey were included for our final analysis. Air pollutants included particulate matter with an aerodynamic diameter ≤ 10 μm (PM10), nitrogen dioxide (NO2), and carbon monoxide (CO). We measured serum 25-hydroxyvitamin D concentration to assess vitamin D status for each participant. Multiple linear and logistic regression analyses were performed to identify associations between ambient air pollution and vitamin D status in each subgroup according to body mass index level.ResultsThe annual average concentrations of PM10, NO2, and CO were significantly associated with a lower serum vitamin D concentration and higher risk of vitamin D deficiency. The results show a significant association between serum vitamin D status and PM10 exposure in obese subgroup. Based on the gender, females with obesity showed more strong association (negative) between different air pollutants and low serum vitamin D concentration and a higher risk of vitamin D deficiency. However, this pattern was not observed in men.ConclusionsThis study provides the first evidence that women with obesity may be more vulnerable to vitamin D deficiency in the context of persistent exposure to air pollution.

The neighbourhood environment and profiles of the metabolic syndrome

BackgroundThere is a dearth of studies on how neighbourhood environmental attributes relate to the metabolic syndrome (MetS) and profiles of MetS components. We examined the associations of interrelated aspects of the neighbourhood environment, including air pollution, with MetS status and profiles of MetS components.MethodsWe used socio-demographic and MetS-related data from 3681 urban adults who participated in the 3rd wave of the Australian Diabetes, Obesity and Lifestyle Study. Neighbourhood environmental attributes included area socio-economic status (SES), population density, street intersection density, non-commercial land use mix, percentages of commercial land, parkland and blue space. Annual average concentrations of NO2 and PM2.5 were estimated using satellite-based land-use regression models. Latent class analysis (LCA) identified homogenous groups (latent classes) of participants based on MetS components data. Participants were then classified into five metabolic profiles according to their MetS-components latent class and MetS status. Generalised additive mixed models were used to estimate relationships of environmental attributes with MetS status and metabolic profiles.ResultsLCA yielded three latent classes, one including only participants without MetS (“Lower probability of MetS components” profile). The other two classes/profiles, consisting of participants with and without MetS, were “Medium-to-high probability of high fasting blood glucose, waist circumference and blood pressure” and “Higher probability of MetS components”. Area SES was the only significant predictor of MetS status: participants from high SES areas were less likely to have MetS. Area SES, percentage of commercial land and NO2 were associated with the odds of membership to healthier metabolic profiles without MetS, while annual average concentration of PM2.5 was associated with unhealthier metabolic profiles with MetS.ConclusionsThis study supports the utility of operationalising MetS as a combination of latent classes of MetS components and MetS status in studies of environmental correlates. Higher socio-economic advantage, good access to commercial services and low air pollution levels appear to independently contribute to different facets of metabolic health. Future research needs to consider conducting longitudinal studies using fine-grained environmental measures that more accurately characterise the neighbourhood environment in relation to behaviours or other mechanisms related to MetS and its components.

Simulation of thermal power plant source contribution to ambient air concentration in Cam Pha City, Quang Ninh province using AERMOD dispersion model

Cam Pha, the home of three major thermal power plants including Cam Pha, Mong Duong I and Mong Duong II, is one of the most important industrial cities in the North of Vietnam. Air pollution due to stacks emission is the biggest problem threatening Cam Pha City's sustainable development. In this study, the AERMOD modeling system was used to evaluate the impact of the stack emission by the thermal power plants on the ambient atmospheric environment. The maximum 1-HR, 24-HR, 99th percentile and annual average concentrations of TSP, SO2 and NO2 were simulated within the 40x40 km domain of 100x100 m grid spacing with the hourly meteorological data taken from 2018 to 2020. Air dispersion simulation is performed on the observed background gas concentration of the everyday environment. Hourly emission data of 10 primary stack sources of 3 factory groups were used as input data. The simulated spatial distribution of gases indicates the strong fluence of the mountainous topography on the dispersion of stack emission. Results also revealed that the maximum short-term stack emission at ground-level concentrations of SO2 and NO2 are much higher than the national standard, thus raising the risk of severe pollution. TSP pollution is less severe than SO2 and NO2 but still at a dangerous level. Since Cam Pha locates by the East Sea with the prevailing wind is heading northeast and east directions. The annual average concentrations of these pollutants indicate that the high terrain areas at the south and northwest of Cam Pha City, which block the flow of the stack emission, are the most affected regions by exhausted gases from industrial stacks.

Long-term trends in urban NO2 concentrations and associated paediatric asthma incidence: estimates from global datasets

Combustion-related nitrogen dioxide (NO2) air pollution is associated with paediatric asthma incidence. We aimed to estimate global surface NO2 concentrations consistent with the Global Burden of Disease study for 1990-2019 at a 1 km resolution, and the concentrations and attributable paediatric asthma incidence trends in 13 189 cities from 2000 to 2019. We scaled an existing annual average NO2 concentration dataset for 2010-12 from a land use regression model (based on 5220 NO2 monitors in 58 countries and land use variables) to other years using NO2 column densities from satellite and reanalysis datasets. We applied these concentrations in an epidemiologically derived concentration-response function with population and baseline asthma rates to estimate NO2-attributable paediatric asthma incidence. We estimated that 1·85 million (95% uncertainty interval [UI] 0·93-2·80 million) new paediatric asthma cases were attributable to NO2 globally in 2019, two thirds of which occurred in urban areas (1·22 million cases; 95% UI 0·60-1·8 million). The proportion of paediatric asthma incidence that is attributable to NO2 in urban areas declined from 19·8% (1·22 million attributable cases of 6·14 million total cases) in 2000 to 16·0% (1·24 million attributable cases of 7·73 million total cases) in 2019. Urban attributable fractions dropped in high-income countries (-41%), Latin America and the Caribbean (-16%), central Europe, eastern Europe, and central Asia (-13%), and southeast Asia, east Asia, and Oceania (-6%), and rose in south Asia (+23%), sub-Saharan Africa (+11%), and north Africa and the Middle East (+5%). The contribution of NO2 concentrations, paediatric population size, and asthma incidence rates to the change in NO2-attributable paediatric asthma incidence differed regionally. Despite improvements in some regions, combustion-related NO2 pollution continues to be an important contributor to paediatric asthma incidence globally, particularly in cities. Mitigating air pollution should be a crucial element of public health strategies for children. Health Effects Institute, NASA.

Research on the Temporal and Spatial Characteristics of Air Pollutants in Sichuan Basin

Sichuan Basin is one of the most densely populated areas in China and the world. Human activities have great impact on the air quality. In order to understand the characteristics of overall air pollutants in Sichuan Basin in recent years, we analyzed the concentrations of six air pollutants monitored in 22 cities during the period from January 2015 to December 2020. During the study period, the annual average concentrations of CO, NO2, SO2, PM2.5 and PM10 all showed a clear downward trend, while the ozone concentration was slowly increasing. The spatial patterns of CO and SO2 were similar. High-concentration areas were mainly located in the western plateau of Sichuan Basin, while the concentrations of NO2 and particulate matter were more prominent in the urban agglomerations inside the basin. During the study period, changes of the monthly average concentrations for pollutants (except for O3) conformed to the U-shaped pattern, with the highest in winter and the lowest in summer. In the southern cities of the basin, secondary sources had a higher contribution to the generation of fine particulate matter, while in large cities inside the basin, such as Chengdu and Chongqing, air pollution had a strong correlation with automobile exhaust emissions. The heavy pollution incidents observed in the winter of 2017 were mainly caused by the surrounding plateau terrain with typical stagnant weather conditions. This finding was also supported by the backward trajectory analysis, which showed that the air masses arrived in Chengdu were mainly from the western plateau area of the basin. The results of this study will provide a basis for the government to take measures to improve the air quality in Sichuan Basin.

The effects of historical residential redlining on temporal trends of air pollution levels measured near New York City schools

BACKGROUND AND AIM: In the 1930’s the Home Owners’ Loan Corporation began categorizing neighborhoods based on racial demographics and perceived risk for mortgage investment. This historical practice of redlining has perpetuated racial segregation, poverty and influenced where major sources of air pollution were placed thus placing some schoolchildren at risk of high exposures. While a declining trend of air pollution was reported in New York City (NYC) between 2001 and 2012, little is known about regional differences in air quality improvement. Our objective was to determine recent temporal trends of air pollution from 2009-2018 near NYC schools and examined if trends differed in historically redlined neighborhoods. METHODS: We examined annual average street-level concentrations of BC, PM2.5, and NO2, within a 250-km radius around NYC public schools (2009-2018; N=1828) using NYC Community Air Survey land-use regression models. Neighborhoods were characterized as redlined (N=892) vs. other (N=936). Year of monitoring (interval variable) and historical redlining (binary) were included in linear regression models. To test whether temporal trends of air pollution differ by historical redlining, a multiplicative interaction term was included. Models were further stratified by historical redlining. Annual percent change (APC) in pollutant concentration was calculated. RESULTS:Overall, there was a decreasing trend of BC (APC=-4.9%), PM2.5 (-4.2%), and NO2 (-3.2%). On average, pollutants were higher in redlined vs. other neighborhoods (BC: 1.12±0.29 vs. 0.99±0.27 µg/m3; PM2.5: 9.47±1.47 vs. 8.93±1.42 µg/m3; NO2: 23.5±4.2 vs. 21.4±3.75 ppb; p0.001 for all). A smaller reduction of BC and PM2.5 was observed in redlined neighborhoods, compared to others for BC (APC: -4.7% vs. -5.1%; P-interaction0.001) and PM2.5 (-4.1% vs. -4.3%; P-interaction0.001) but not NO2. CONCLUSIONS:Despite significant reductions in recent annual average BC, PM2.5, and NO2 concentrations across NYC, historically redlined neighborhoods experienced smaller decrease in air pollution compared to others, highlighting a potential ongoing legacy of the discriminatory practice. KEYWORDS: black carbon, nitrogen dioxide, particulate matter, health equity, environmental justice

The relationship between urban heat island and air pollutants and them with influencing factors in the Yangtze River Delta, China

The relationship between urban heat island (UHI) and air pollution is worth exploring. We used land surface temperature (LST) and air quality data investigated the spatio-temporal distribution of UHI intensity (UHII) and six air pollutants of carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), fine particle matter (PM2.5), coarse particle matter (PM10) and sulfur dioxide (SO2) in the YRD during 2015–2019, and the correlation relationship between UHII and air pollutants concentration. Using regression analysis to analyze the influence of natural and human factors on the UHI and air pollutants. We found that: (1) Annual average daytime UHII was 1.20 ℃ and northern higher than southern cities. Nighttime UHII was 0.35 ℃ and southern higher than northern. Annual average CO, NO2, O3, PM2.5, PM10 and SO2 concentrations were 0.83 mg/m3, 35.41 ug/m3, 63.34 ug/m3, 44.16 ug/m3, 73.46 ug/m3, 13.86 ug/m3, respectively. Air pollution in inland cities were worse than coastal cities. (2) A significant positive correlation between daytime UHII and O3 concentration, and a negative correlation with other pollutants. (3) The influence of LST is the closest factors, followed by vegetation coverage, geographic location, elevation and economic development intensity on UHII and air pollution.

Commute patterns, residential traffic-related air pollution, and lung cancer risk in the prospective UK Biobank cohort study

IntroductionCommuting exposes millions of people to carcinogens from traffic-related air pollution (TRAP) but is seldomly considered in epidemiologic studies of lung cancer. In the prospective United Kingdom (UK) Biobank cohort study, we investigated associations between commute patterns, residential nitrogen dioxide concentrations (NO2; a surrogate for TRAP), and lung cancer risk. MethodsWe analyzed 234,124 employed participants at baseline (2006–2010). There were 493 incident lung cancer cases diagnosed over an average 7-year follow-up. Subjects were cross-classified into exclusive categories of commute mode (automobile, public transportation, walking, cycling, active mixture, and other mixture) and frequency (regular: 1–4, often: ≥5 work-bound trips/week). Annual average residential NO2 concentrations in 2005–2007 were estimated with land use regression. Multivariable Cox regression was used to estimate associations between commute patterns, NO2 quartiles, and incident lung cancer. We conducted analyses stratified by NO2 (>, ≤median = 28.3 µg/m3) and potential confounders such as sex and smoking. ResultsCompared to regular automobile use, commuting often by public transportation was associated with increased lung cancer risk (hazard ratio (HR) = 1.58, 95% confidence intervals (CI):1.08–2.33). Additionally, we found a positive exposure–response relationship with residential NO2 (HRQ2 = 1.21, 95 %CI: 0.90–1.62; HRQ3 = 1.48, 95 %CI: 1.10–1.99; HRQ4 = 1.58, 95 %CI: 1.13–2.23; p-trend = 3.1 × 10−3).The public transportation association was observed among those with higher NO2 (p-interaction = 0.02). Other commute categories were not associated with risk. ConclusionsCommuters residing in high-NO2 areas who often use public transportation could have elevated lung cancer risk compared to regular automobile users. These results warrant investigations into which component(s) of public transportation contribute to the observed association with increased lung cancer risk.

Relationship between urban atmospheric environment and surrounding two-dimensional and three-dimensional landscape pattern in China.

Atmospheric environment in urban built-up area is severely influenced by the surrounding landscape pattern. Understanding the relationship between air pollution and surrounding landscape pattern at small scale has great significance for mitigating air pollution from the perspective of urban construction. The annual average concentrations of NO2, SO2, PM2.5 and PM10 from 266 air pollution monitoring stations in 30 provincial capitals of China in 2017 were chosen as dependent variables. Ten two-dimensional and three-dimensional landscape pattern indices (number of buildings, building aggregation, building density, impervious water ratio, quantitative density of catering, building footprint area, high building ratio, floor area ratio, total building area and building type Shannon diversity index) within the 3 km area around the monitoring stations were used as independent variables. The effects of landscape pattern on the concentration of four air pollutants were analyzed using the boosted regression trees model. The results showed that the concentration of four air pollutants in the central and northern cities were significantly higher than that in the southeast coastal cities and southwest cities. The most important factor affecting the concentrations of NO2, SO2, PM2.5 and PM10 was the impervious ratio, with relative contribution rates of 40.7%, 36.3%, 51.0% and 51.8% respectively. The results of sub-region analysis showed that the most important influencing factor differed in different regions, including the impervious ratio in the East and Central China; the number and density of buildings in South China; the impervious ratio and diversity of building types in North China; the impervious ratio and the number of buildings in Northeast China, the density of buildings in Northwest China. Such differences were mainly caused by climate, topography, urban planning, and other factors.