Annual Mean NO2 Research Articles

How do emission reductions of individual national and local measures impact street-level air quality in a neighbourhood of Madrid, Spain?

AbstractThis paper aims to assess the impact of individual measures for NOx emission reduction on NO2 concentrations at very high spatial resolution in an urban district of Madrid City (Spain). A methodology based on a set of Computational Fluid Dynamics simulations for 16 meteorological scenarios combined with the CHIMERE model for background pollution is used to obtain annual NO2 concentration maps. Two scenarios included in the Spanish National Air Pollution Control Programme are investigated: NOx emission reductions from the installation of more efficient boilers for domestic heating (ECOBOIL) and from the partly substitution of passenger cars with combustion engines by electric cars (EC). This analysis is extended to 9 additional scenarios of more ambitious implementation of electric vehicles in order to determine what the NOx emission reduction required for the annual mean NO2 concentration EU limit value not being exceeded is. The ECOBOIL scenario has a very weak impact on the NO2 concentrations. However, the EC scenario implies a more significant reduction of the NO2 concentrations, but not enough to fully remove NO2 limit value exceedances in the study area. A small additional (compared with the EC scenario) implementation of electric vehicles seems to fulfil that the spatially averaged NO2 concentration be lower than the EU limit value, but the area with exceedances is still very large. However, stronger traffic emission reductions (80%) corresponding to the most ambitious scenarios are needed in order to reach that at least 95% of the domain is free of EU limit value exceedances.

Neighborhood-scale air quality, public health, and equity implications of multi-modal vehicle electrification

Electric vehicles (EVs) constitute just a fraction of the current U.S. transportation fleet; however, EV market share is surging. EV adoption reduces on-road transportation greenhouse gas emissions by decoupling transportation services from petroleum, but impacts on air quality and public health depend on the nature and location of vehicle usage and electricity generation. Here, we use a regulatory-grade chemical transport model and a vehicle-to-electricity generation unit electricity assignment algorithm to characterize neighborhood-scale (∼1 km) air quality and public health benefits and tradeoffs associated with a multi-modal EV transition. We focus on a Chicago-centric regional domain wherein 30% of the on-road transportation fleet is instantaneously electrified and changes in on-road, refueling, and power plant emissions are considered. We find decreases in annual population-weighted domain mean NO2 (−11.83%) and PM2.5 (−2.46%) with concentration reductions of up to −5.1 ppb and −0.98 µg m−3 in urban cores. Conversely, annual population-weighted domain mean maximum daily 8 h average ozone (MDA8O3) concentrations increase +0.64%, with notable intra-urban changes of up to +2.3 ppb. Despite mixed pollutant concentration outcomes, we find overall positive public health outcomes, largely driven by NO2 concentration reductions that result in outsized mortality rate reductions for people of color, particularly for the Black populations within our domain.

Impacts of net zero policies on air quality in a metropolitan area of the United Kingdom: Towards world health organization air quality guidelines

Climate change and air pollution are closely interlinked since carbon dioxide and air pollutants are co-emitted from fossil fuel combustion. Net Zero (NZ) policies aiming to reduce carbon emissions will likely bring co-benefits in air quality and associated health. However, it is unknown whether regional NZ policies alone will be sufficient to reduce air pollutant levels to meet the latest 2021 World Health Organisation (WHO) guidelines. Here, we carried out high resolution air quality modelling for in the West Midlands region, a typical metropolitan area in the UK, to quantify the effects of different NZ policies on air quality. Results show that NZ policies will significantly improve air quality in the West Midlands, with up to 6 μg m−3 (21%) reduction in annual mean NO2 (mostly through the electrification of vehicle fleet, EV) and up to 1.4 μg m−3 (12%) reduction in annual mean PM2.5 projected for 2030 relative to levels under a “business as usual” (BAU) scenario. Under BAU, 2030 PM2.5 concentrations in most wards would be below 10 μg m−3 whilst under the Net Zero scenario, those in all wards would be below 10 μg m−3. This means that the ward averages in the West Midlands would meet the UK PM2.5 of 10 μg m−3target a decade early under the Net Zero scenario. However, no ward-level-averaged annual mean PM2.concentrations meet the 2021 WHO Air Quality guideline level of 5 μg m−3 under any scenario. Similarly for NO2 only 18 wards (8% of the region's population) are predicted to have NO2 concentrations below the 2021 WHO guideline level (10 μg m−3). Decarbonisation policies linked to Net Zero deliver substantial regional air quality benefits, but are not in isolation sufficient to deliver clean air with air pollutant levels low enough to meet the 2021 WHO guidelines.

Incident risk and burden of cardiovascular diseases attributable to long-term NO2 exposure in Chinese adults

BackgroundA number of studies suggested a nexus between long-term exposure to nitrogen dioxide (NO2) and the incidence of cardiovascular disease (CVD), while population-based cohort evidence in low- and middle-income countries was extensively sparse. MethodsWe carried out an 8-year longitudinal study (2010–2018) in a nationwide dynamic cohort of 36,948 Chinese adult participants, who were free of CVD at baseline. Annual average estimates of NO2 exposure were predicted using a well-validated spatiotemporal model and assigned to study participants based on their residential counties. Considering death as a competing risk event, Fine-Gray competing risk models with time-varying exposures at an annual scale were used to quantify incident risks of overall CVD, hypertension, and stroke associated with a 10-μg/m3 rise in NO2 exposure. Using the meta-analysis approach, we performed a pooled analysis of hazard ratio (HR) drawn from this and prior multinational cohort studies for the assessment of attributable burden. NO2-attributable overall CVD incidents in China were evaluated by city and province for years 2010 and 2018, referring to a counterfactual exposure level of 10 μg/m3 (2021 World Health Organization [WHO] air quality guidelines). A decomposition method was used to decompose net change in NO2-attributable CVD incidents during 2010 and 2018 into 3 primary contributions of driving factors (i.e., changes in NO2 exposure, population size, and incidence rate). ResultsA total of 4428 overall CVD events (hypertension 2448, stroke 1044) occurred during a median follow-up period of 6.1 years. Annual mean NO2 concentration from 2010 to 2018 was 20.0 μg/m3 (range: 6.9–57.4 μg/m3). An increase of 10-µg/m3 in NO2 was associated with an HR of 1.558 (95% confidence interval [CI]: 1.477, 1.642) for overall CVD, 1.521 (95% CI: 1.419, 1.631) for hypertension, and 1.664 (95% CI: 1.485, 1.865) for stroke. Longitudinal associations of NO2 exposure with incident CVD were nearly linear over the exposure range, suggesting no discernible thresholds. Subgroup analyses indicated significantly higher NO2-associated risks of incident CVD among urban residents and overweight/obese individuals. According to pooled HR of NO2-CVD association (1.108, 95% CI: [1.007, 1.219]) from 10 multinational cohort studies, we estimated totally 1.44 million incident CVD cases attributable to NO2 exposure in 2018, representing a substantial decrease of 0.41 million compared to the estimate in 2010 (1.85 million) in mainland of China. Nationally, from 2010 to 2018, the attributable incident cases greatly dropped by 22.4%, which was dominantly driven by declined NO2 concentration (−47.1%) that had offset far from the rise of CVD incidence rate (+19.6%) and population growth (+5.1%). ConclusionsThis study provided nationwide cohort evidence for elevated risks of CVD incidence associated with long-term ambient NO2 exposure among Chinese adults, particularly in urban areas and among overweight/obese individuals. Our findings highlighted that reducing NO2 exposure below 2021 WHO guideline could help prevent a substantial portion of incident CVD cases in China.

Causal links between long-term exposure to NO2 and the risk of cardiovascular hospitalization

Objective: To determine the causal association between long-term Nitrogen dioxide (NO2) exposure and the risk of cardiovascular hospitalization. Methods: Based on a sub-cohort of a community-based prospective cohort study, a total of 36 271 participants were recruited from 35 communities randomly selected in Guangzhou in 2015. The annual average exposure of NO2, demographic characteristics, lifestyle factors, and information on the causes of hospitalization was collected. We applied marginal structural Cox models to investigate the effect of NO2 on cardiovascular hospitalization. Demographic and behavioral factors also stratified results. Results: The mean age of participants in the present study was (50.9±17.8) years, and the cardiovascular admission rate was 8.7%, with 203 822 person-years of follow-up. The annual mean NO2 concentration was 48.7 μg/m3 during 2015-2020. For each 10 μg/m3 increase in NO2 concentrations, the HRs (95%CIs) of total cardiovascular hospitalization, cardiovascular hospitalization, and cerebrovascular hospitalization were 1.33 (1.16-1.52), 1.36 (1.16-1.60) and 1.25 (1.00-1.55), respectively. Participants who were never married/married, with secondary education, high exercise frequency, or non-smokers/current smokers may be more susceptible than their counterparts. Conclusion: Long-term exposure to NO2 significantly increased hospitalization risk for cardiovascular disease.

A global spatial-temporal land use regression model for nitrogen dioxide air pollution

Introduction: The World Health Organization (WHO) recently revised its health guidelines for Nitrogen dioxide (NO2) air pollution, reducing the annual mean NO2 level to 10 μg/m3 (5.3 ppb) and the 24-h mean to 25 μg/m3 (13.3 ppb). NO2 is a pollutant of global concern, but it is also a criteria air pollutant that varies spatiotemporally at fine resolutions due to its relatively short lifetime (~hours). Current models have limited ability to capture both temporal and spatial NO2 variation and none are available with global coverage. Land use regression (LUR) models that incorporate timevarying predictors (e.g., meteorology and satellite NO2 measures) and land use characteristics (e.g., road density, emission sources) have significant potential to address this need.Methods: We created a daily Land use regression model with 50 × 50 m2 spatial resolution using 5.7 million daily air monitor averages collected from 8,250 monitor locations.Results: In cross-validation, the model captured 47%, 59%, and 63% of daily, monthly, and annual global NO2 variation. Daily, monthly, and annual root mean square error were 6.8, 5.0, and 4.4 ppb and absolute bias were 46%, 30%, and 21%, respectively. The final model has 11 variables, including road density and built environments with fine (30 m or less) spatial resolution and meteorological and satellite data with daily temporal resolution. Major roads and satellite-based estimates of NO2 were consistently the strongest predictors of NO2 measurements in all regions.Discussion: Daily model estimates from 2005–2019 are available and can be used for global risk assessments and health studies, particularly in countries without NO2 monitoring.

Spatiotemporal Variation of Nitrogen Dioxide and Nighttime Light Dataset of Iranian Metropolises in the COVID-19 Outbreak

COVID-19 lockdown has caused a reduction in traffic volume and industrial activities which are the main sources of air pollution in whole of the world. As tropospheric NO2 pollutant and nighttime light (NTL) are the representative of human activities, this study focused to quantify the annual and monthly change of NO2 concentration and NTL in 14 metropolises of Iran before, during and after the lockdown months such as March, April, October and November. TROPOMI images of Sentinel-5p were used for investigation of NO2 column density in 2019, 2020 and 2021, and the variation of NTL was monitored by VIIRS images. The findings showed the majority of metropolises have an increase of NO2 concentration in March and October and a decrease in April and November in 2020 but a significant increase in 2021. The similar pattern of NTL change as NO2 was observed in the most metropolises. The correlation coefficient between NO2 concentration and NTL was calculated from 0.66 to 0.75. So, in majority of metropolises, the reduction of NO2 was observed with reduction of NTL. According to the results, reducing traffic volume as mobile source does not has an effective contribution in NO2 emission in some metropolises of Iran which the stationary sources are dominant such as Isfahan. Tehran as the capital of Iran showed the highest annual mean NO2 reduction in lockdown, this finding showed the important role of traffic volume on air quality of Tehran compared to industrial activities. The integrated application of TROPOMI and NTL data will help to better decision making for controlling and managing of air quality in country’s urban area.

Excess deaths associated with long-term exposure to ambient NO2 in China

World Health Organization updated the target of annual mean NO2 from 40 μg m−3 to 10 μg m−3 in 2021 based on new evidences that showed statistically independent effects of long-term exposure to ambient NO2 and mortality. We estimate the excess deaths associated with long-term exposure to ambient NO2 (DAAN) from non-accidental disease (NAD), cardiovascular disease (CVD) and respiratory disease (RD) in China in 2013–2020 using a counterfactual analytic framework adopted by Global Burden Disease. We use relative risk (RR) values based on a meta-analysis, and assume a linear concentration-response (C-R) function and a threshold value of 10 μg m−3. DAAN from NAD vary in 279 (95% Cl: 189–366) to 339 (95% Cl: 231–442) thousand in 2013–2020, comparable to excess deaths attributed to long-term exposure to ambient O3 in China. DAAN from NAD changes by −43%–+220% using different RR values, C-R function shapes, and threshold values. DAAN from RD reduces while those from CVD increases significantly in 2013–2020. DAAN from RD account for ∼10% of total DAAN. We found that the reduction of NO2 concentration and baseline mortality of NAD offsets the adverse effects of population growth and aging in 2013–2017. In 2017–2020, the contribution from air pollution reduction surpasses the adverse effects of population growth and aging in most regions. We suggest that continuing the reduction rate of NO2 emissions in 2013–2020 would offset the effects of population growth and aging in 2020–2030. Faster reduction of NO2 sources is required to reduce DAAN in Liaoning, Shanxi, Shanghai, and Anhui, where DAAN from NAD, CVD and RD increased in 2013–2020. Our results should be interpreted with caution because the causality support of long-term exposure to ambient NO2 on mortality from toxicological studies is not firm yet, and NO2 is possibly just a marker of traffic-related pollutants.

Are dense networks of low-cost nodes really useful for monitoring air pollution? A case study in Staffordshire

Abstract. Air pollution exhibits hyper-local variation, especially near emissions sources. In addition to people's time-activity patterns, this variation is the most critical element determining exposure. Pollution exposure is time-activity- and path-dependent, with specific behaviours such as mode of commuting and time spent near a roadway or in a park playing a decisive role. Compared to conventional air pollution monitoring stations, nodes containing low-cost air pollution sensors can be deployed with very high density. In this study, a network of 18 nodes using low-cost air pollution sensors was deployed in Newcastle-under-Lyme, Staffordshire, UK, in June 2020. Each node measured a range of species including nitrogen dioxide (NO2), ozone (O3), and particulate matter (PM2.5 and PM10); this study focuses on NO2 and PM2.5 over a 1-year period from 1 August 2020 to 1 October 2021. A simple and effective temperature, scale, and offset correction was able to overcome data quality issues associated with temperature bias in the NO2 readings. In its recent update, the World Health Organization (WHO) dramatically reduced annual exposure limit values from 40 to 10 µg m−3 for NO2 and from 10 to 5 µg m−3 for PM2.5. We found that the average annual mean NO2 concentration for the network was 17.5 µg m−3 and 8.1 µg m−3 for PM2.5. While in exceedance of the WHO guideline levels, these average concentrations do not exceed legally binding UK/EU standards. The network average NO2 concentration was 12.5 µg m−3 higher than values reported by a nearby regional air quality monitoring station, showing the critical importance of monitoring close to sources before pollution is diluted. We demonstrate how data from a low-cost air pollution sensor network can reveal insights into patterns of air pollution and help determine whether sources are local or non-local. With spectral analysis, we investigate the variation of the pollution levels and identify typical periodicities. Both NO2 and PM2.5 have contributions from high-frequency sources; however, the low-frequency sources are significantly different. Using spectral analysis, we determine that at least 54.3±4.3 % of NO2 is from local sources, whereas, in contrast, only 37.9±3.5 % of PM2.5 is local.

Air quality in Germany as a contributing factor to morbidity from COVID-19

BackgroundThe SARS-CoV-2 virus has been spreading in Germany since January 2020, with regional differences in incidence, morbidity, and mortality. Long-term exposure to air pollutants as nitrogen dioxide (NO2), nitrogen monoxide (NO), ozone (O3), and particulate matter (<10 μm PM10, <2.5 μm PM2.5) has a negative impact on respiratory functions. We analyze the association between long-term air pollution and the outcome of SARS-CoV-2 infections in Germany. MethodsWe conducted an observational study in Germany on county-level, investigating the association between long-term (2010–2019) air pollutant exposure (European Environment Agency, AirBase data set) and COVID-19 incidence, morbidity, and mortality rate during the first outbreak of SARS-CoV-2 (open source data Robert Koch Institute). We used negative binominal models, including adjustment for risk factors (age, sex, days since first COVID-19 case, population density, socio-economic and health parameters). ResultsAfter adjustment for risk factors in the tri-pollutant model (NO2, O3, PM2.5) an increase of 1 μg/m³ NO2 was associated with an increase of the need for intensive care due to COVID-19 by 4.2% (95% CI 1.011–1.074), and mechanical ventilation by 4.6% (95% CI 1.010–1.084). A tendency towards an association of NO2 with COVID-19 incidence was indicated, as the results were just outside of the defined statistical significance (+1.6% (95% CI 1.000–1.032)). Long-term annual mean NO2 level ranged from 4.6 μg/m³ to 32 μg/m³. ConclusionsOur results indicate that long-term NO2 exposure may have increased susceptibility for COVID-19 morbidity in Germany. The results demonstrate the need to reduce ambient air pollution to improve public health.

Reduced fertilization mitigates N2O emission and drip irrigation has no impact on N2O and NO emissions in plastic-shed vegetable production in northern China

Plastic-shed vegetable production in China creates hotspots for emission of the potent greenhouse gas nitrous oxide (N2O) and the atmospheric pollutant nitric oxide (NO). To mitigate N2O and NO emissions, determination of the predominant processes of N2O and NO generation in plastic-shed vegetable production is important. Here, we reported the findings of a 2-year experimental study on the effects of reduced fertilization and/or drip irrigation on N2O and NO emissions during plastic-shed tomato production in northern China. Five treatments were applied: 1) over fertilization and flood irrigation (conventional practice); 2) fertilization reduced by 20% and flood irrigation; 3) fertilization reduced by 20% and drip irrigation; 4) fertilization reduced by 30% and drip irrigation, and 5) control (no fertilizer input and flood irrigation). Reduced both basal and top-dressed fertilization maintained tomato yields. Compared with conventional practices (mean annual N2O and NO emissions: 18.1 ± 1.3 and 0.79 ± 0.02 kg N ha−1 yr −1, respectively), fertilization reduction by 20%–30% decreased the annual N2O emission by 21.2%–27.0% owing to lower soil inorganic nitrogen (SIN) contents under the reduced fertilization practices. Switching from flood to drip irrigation might weaken denitrification due to lower soil moisture and less wet soil area, but increased SIN contents, and thus had no significant impact on annual N2O and NO emissions. Peak N2O fluxes occurred at soil temperature 28 °C and water-filled pore space (WFPS) > 60%, were higher than those for NO, and peak NO fluxes appeared 4–6 days later than N2O fluxes, consistent with the decline in WFPS. These observations indicated that N2O and NO from alkaline plastic-shed soil may be mainly generated via heterotrophic denitrification and nitrification, respectively. Reduced fertilization and drip irrigation in plastic-shed tomato production maintained crop productivity and mitigated N2O emission. These results could be integrated into the decision-making in sustainable plastic-shed production.

High Spatial Resolution Assessment of the Effect of the Spanish National Air Pollution Control Programme on Street-Level NO2 Concentrations in Three Neighborhoods of Madrid (Spain) Using Mesoscale and CFD Modelling

Current European legislation aims to reduce the air pollutants emitted by European countries in the coming years. In this context, this article studies the effects on air quality of the measures considered for 2030 in the Spanish National Air Pollution Control Programme (NAPCP). Three different emission scenarios are investigated: a scenario with the emissions in 2016 and two other scenarios, one with existing measures in the current legislation (WEM2030) and another one considering the additional measures of NAPCP (WAM2030). Previous studies have addressed this issue at a national level, but this study assesses the impact at the street scale in three neighborhoods in Madrid, Spain. NO2 concentrations are modelled at high spatial resolution by means of a methodology based on computational fluid dynamic (CFD) simulations driven by mesoscale meteorological and air quality modelling. Spatial averages of annual mean NO2 concentrations are only estimated to be below 40 µg/m3 in all three neighborhoods for the WAM2030 emission scenarios. However, for two of the three neighborhoods, there are still zones (4–12% of the study areas) where the annual concentration is higher than 40 µg/m3. This highlights the importance of considering microscale simulations to assess the impacts of emission reduction measures on urban air quality.

Estimation and Spatiotemporal Analysis of NO2 Pollution in East Asia During 2001–2016

AbstractAmbient nitrogen dioxide (NO2) not only has adverse health effects on humans but also contributes to the production of two major secondary atmospheric pollutants, ozone (O3) and fine particulate matter (PM2.5). In this study, surface NO2 concentrations in East Asia from 2001 to 2016 were estimated by combining an ensemble backpropagation neural network method, satellite NO2 column data, and reanalysis data. The estimated monthly and annual mean NO2 concentrations were well‐correlated with the observations, with R (correlation coefficient) values of 0.89 and 0.91, respectively. Our results indicate that the NO2 concentrations in most areas of East Asia peaked during 2011–2013. The NO2 concentrations in autumn and winter, especially in the eastern and northern parts of China, were much higher than those in summer. In terms of population NO2 exposure, over 25 million South Korea residents (∼45% of the population) were exposed to NO2 concentrations higher than the 2005 World Health Organization's annual standard (40 μg/m3, ∼22 ppbv at 25°C) in 2015. In contrast, the entire populations of some developing countries, such as Vietnam, Cambodia, Myanmar, the Philippines, and Lao PDR, were exposed to NO2 concentrations less than 14 ppbv. Based on the estimation, NO2‐related asthma cases in East Asia increased by 1.37% annually from 2001 to 2015, reaching 139,000 cases (95% confidence interval: 37,400–263,400) in 2015. NOx emission inventories vary from country to country in East Asia; thus, more targeted NOx emission‐control policies are urgently required.

First satellite-based regional hourly NO2 estimations using a space-time ensemble learning model: A case study for Beijing-Tianjin-Hebei Region, China

Surface Nitrogen dioxide (NO2) concentrations have been generated with satellite retrievals using multiple statistical algorithms. However, they are often given at coarse frequencies (“snapshot”, daily or even longer), limiting their applications in epidemiological studies and assessing the evolution of NO2 pollution. This study investigated the potential applicability of Himawari-8 derived hourly fine particulate matter concentrations in producing hourly NO2 concentrations by constructing a space-time ensemble model. The Beijing-Tianjin-Hebei (BTH) region, one of the serious pollution regions in China, is the study region chosen. The proposed model performs well in estimating hourly NO2 concentration with a high cross-validation (CV) coefficient of determination (R2 = 0.81) and low CV root-mean-square (RMSE = 9.71 μg/m3), mean prediction errors (MPE = 6.33 μg/m3), and relative prediction errors (RPE = 22.5%). On daily, monthly, seasonal, and annual time scales, CV R2 increases to 0.89, 0.93, 0.97, and 0.99, respectively. The annual mean model estimated NO2 concentration over BTH region is 28.2 ± 6.5 μg/m3, with relatively higher NO2 concentrations are seen in southern and southeastern BTH. Winter experiences the most severe NO2 concentrations, followed by autumn, spring, and summer. Surface NO2 concentrations are higher (lower) in the morning (afternoon) and tend to decrease gradually with time. The model generally captures the hourly evolution of NO2 concentrations for the severe pollution episode but shows some underestimations. The annual mean NO2 concentrations were 2.8% lower on the weekend than on weekdays. In addition, the weekend effects of NO2 concentrations are larger at rush hour and lower in the noon. The hourly NO2 products derived from proposed approach are potentially useful for improving our understanding of the source, evolution, and transportation behavior of NO2 pollution episodes and for exposure- and health-related research. The proposed approach also enriches the potential applications of geostationary satellites (e.g., Himawari-8).

Downscaling of air pollutants in Europe using uEMEP_v6

Abstract. The air quality downscaling model uEMEP and its combination with the EMEP MSC-W chemical transport model are used here to achieve high-resolution air quality modelling at street level in Europe. By using publicly available proxy data, this uEMEP–EMEP modelling system is applied to calculate annual mean NO2, PM2.5, PM10, and O3 concentrations for all of Europe down to 100 m resolution and is validated against all available AIRBASE monitoring stations in Europe at 25 m resolution. Downscaling is carried out on annual mean concentrations, requiring special attention to non-linear processes, such as NO2 chemistry for which frequency distributions are applied to better represent the non-linear NO2 chemistry. The downscaling shows significant improvement in NO2 concentrations for which the spatial correlation has been doubled for most countries and bias reduced from −46 % to −18 % for all stations in Europe. The downscaling of PM2.5 and PM10 does not show improvement in spatial correlation but does reduce the overall bias in the European calculations from −21 % to −11 % and from −39 % to −30 % for PM2.5 and PM10, respectively. There is improved spatial correlation in most countries after downscaling of O3 and a reduced positive bias of O3 concentrations from +16 % to +11 %. Sensitivity tests in Norway show that improvements in the emission and emission proxy data used for the downscaling can significantly improve both the NO2 and PM results. The downscaling development opens the way for improved exposure estimates, improved assessment of emissions, and detailed calculations of source contributions to exceedances in a consistent way for all of Europe at high resolution.

A multi-step machine learning approach to assess the impact of COVID-19 lockdown on NO2 attributable deaths in Milan and Rome, Italy

BackgroundAir pollution is one of the main concerns for the health of European citizens, and cities are currently striving to accomplish EU air pollution regulation. The 2020 COVID-19 lockdown measures can be seen as an unintended but effective experiment to assess the impact of traffic restriction policies on air pollution. Our objective was to estimate the impact of the lockdown measures on NO2 concentrations and health in the two largest Italian cities.MethodsNO2 concentration datasets were built using data deriving from a 1-month citizen science monitoring campaign that took place in Milan and Rome just before the Italian lockdown period. Annual mean NO2 concentrations were estimated for a lockdown scenario (Scenario 1) and a scenario without lockdown (Scenario 2), by applying city-specific annual adjustment factors to the 1-month data. The latter were estimated deriving data from Air Quality Network stations and by applying a machine learning approach. NO2 spatial distribution was estimated at a neighbourhood scale by applying Land Use Random Forest models for the two scenarios. Finally, the impact of lockdown on health was estimated by subtracting attributable deaths for Scenario 1 and those for Scenario 2, both estimated by applying literature-based dose–response function on the counterfactual concentrations of 10 μg/m3.ResultsThe Land Use Random Forest models were able to capture 41–42% of the total NO2 variability. Passing from Scenario 2 (annual NO2 without lockdown) to Scenario 1 (annual NO2 with lockdown), the population-weighted exposure to NO2 for Milan and Rome decreased by 15.1% and 15.3% on an annual basis. Considering the 10 μg/m3 counterfactual, prevented deaths were respectively 213 and 604.ConclusionsOur results show that the lockdown had a beneficial impact on air quality and human health. However, compliance with the current EU legal limit is not enough to avoid a high number of NO2 attributable deaths. This contribution reaffirms the potentiality of the citizen science approach and calls for more ambitious traffic calming policies and a re-evaluation of the legal annual limit value for NO2 for the protection of human health.

Trend reversal from source region to remote tropospheric NO2 columns.

Global tropospheric nitrogen dioxide (NO2) changes have different or even opposite impacts on the photochemical formation of ozone in different regions under different weather and emission condition. However, the changes over regions affected by different levels of human activities are not well known. By using the Ozone Monitoring Instrument (OMI) measurements, we analyzed spatial and temporal variability of tropospheric NO2 vertical column densities (VCDs) from the megacity to the background regions during 2005-2019. Consistent with previous research, our results show a rapid decline of tropospheric NO2 column density over regions strongly affected by human activities, especially for source regions. The decline rates of annual mean NO2 VCDs are up to - 2.44% year-1, - 2.37% year-1, and - 1.43% year-1 over megacities of the USA, Europe, and China, respectively. However, the decreasing rate has slowed, and even reversed to an increasing trend, of tropospheric NO2 from megacities to developing and remote regions, especially over ocean and background areas less affected by anthropogenic activity. From 2005 to 2019, the NO2 VCDs over the ocean and background areas increased for all seasons, with the statistically significant (p < 0.05) trends of 1.15%/0.74% year-1 (MAM), 1.20%/1.06% year-1 (JJA), 1.16%/0.82% year-1 (SON), and 0.68%/0.65% year-1 (DJF), respectively, for ocean/background region. Such decreasing/increasing trends of tropospheric NO2 over sources/remote regions may prevent the ozone air pollution to be effectively resolved to achieve air quality goals worldwide.

Identification of NO2 and SO2 Pollution Hotspots and Sources in Jiangsu Province of China

Nitrogen dioxide (NO2) and sulfur dioxide (SO2) are important atmospheric trace gases for determining air quality, human health, climate change, and ecological conditions both regionally and globally. In this study, the Ozone Monitoring Instrument (OMI), total column nitrogen dioxide (NO2), and sulfur dioxide (SO2) were used from 2005 to 2020 to identify pollution hotspots and potential source areas responsible for air pollution in Jiangsu Province. The study investigated the spatiotemporal distribution and variability of NO2 and SO2, the SO2/NO2 ratio, and their trends, and potential source contribution function (PSCF) analysis was performed to identify potential source areas. The spatial distributions showed higher values (&gt;0.60 DU) of annual mean NO2 and SO2 for most cities of Jiangsu Province except for Yancheng City (&lt;0.50 DU). The seasonal analyses showed the highest NO2 and SO2 in winter, followed by spring, autumn, and summer. Coal-fire-based room heating and stable meteorological conditions during the cold season may cause higher NO2 and SO2 in winter. Notably, the occurrence frequency of NO2 and SO2 of &gt;1.2 was highest in winter, which varied between 9.14~32.46% for NO2 and 7.84~21.67% for SO2, indicating a high level of pollution across Jiangsu Province. The high SO2/NO2 ratio (&gt;0.60) indicated that industry is the dominant source, with significant annual and seasonal variations. Trends in NO2 and SO2 were calculated for 2005–2020, 2006–2010 (when China introduced strict air pollution control policies during the 11th Five Year Plan (FYP)), 2011–2015 (during the 12th FYP), and 2013–2017 (the Action Plan of Air Pollution Prevention and Control (APPC-AC)). Annually, decreasing trends in NO2 were more prominent during the 12th FYP period (2011–2015: −0.024~−0.052 DU/year) than in the APPC-AC period (2013–2017: −0.007~−0.043 DU/year) and 2005–2020 (−0.002 to −0.012 DU/year). However, no prevention and control policies for NO2 were included during the 11th FYP period (2006–2010), resulting in an increasing trend in NO2 (0.015 to 0.031) observed throughout the study area. Furthermore, the implementation of China’s strict air pollution control policies caused a larger decrease in SO2 (per year) during the 12th FYP period (−0.002~−0.075 DU/year) than in the 11th FYP period (−0.014~−0.071 DU/year), the APPC-AC period (−0.007~−0.043 DU/year), and 2005–2020 (−0.015~−0.032 DU/year). PSCF analysis indicated that the air quality of Jiangsu Province is mainly influenced by local pollution sources.

COVID-19 transport restrictions in Ireland: impact on air quality and respiratory hospital admissions

AimExposure to poor air quality is a well-established factor for exacerbation of respiratory system diseases (RSDs); whether air pollutants are a cause of the development of RSD, however, remains unclear. This study aimed to examine the relationship between COVID-19 transport restrictions and hospital admissions because of RSD in Dublin city and county for 2020. Study designThis was a retrospective population-based cohort. MethodsAdmission data were collected from the Health Service Executive Hospital In-patient Enquiry. Daily count of hospital admissions with Dublin city and county address with primary diagnosis of RSD was performed. The daily air nitrogen dioxide (NO2) data were obtained from the Environmental Protection Agency (EPA). ResultsDuring the period of transport restrictions, there was a reduction in the annual mean NO2 from 25 μg/m3 to 17 μg/m3 (P < 0.001), and decreases in hospital admissions for RSD were observed. Among the 9934 patient episodes included in this study, the mean age at admission was 61.5 years, 57.8% were female (n = 5744), and mean (standard deviation) length of stay was 7.5 (13.52) days. ConclusionThis study, using routinely gathered data, suggests that decreases in ambient NO2 as related to COVID-19 transport restrictions were significantly associated with lower asthma and chronic obstructive pulmonary disease admissions.

Introducing the Green Infrastructure for Roadside Air Quality (GI4RAQ) Platform: Estimating Site-Specific Changes in the Dispersion of Vehicular Pollution Close to Source

The benefits of ‘green infrastructure’ are multi-faceted and well-documented, but estimating those of individual street-scale planting schemes at planning can be challenging. This is crucial to avoid undervaluing proposed schemes in cost–benefit analyses, and ensure they are resilient to ‘value engineering’ between planning and implementation. Here, we introduce prototype software enabling urban practitioners to estimate the site-specific air quality impacts of roadside vegetation barriers: highly localised changes in pollutant concentrations due to changes in the dispersion of vehicular emissions close to source. We summarise the recent shift in understanding regarding the impacts of vegetation on urban air pollution towards changes in pollutant dispersion (cf. deposition) and describe our prototype software, offering rapid estimates thereof. First tests of the underlying model’s performance are promising, reproducing: annual mean NO2 and PM2.5 concentrations in a street canyon (Marylebone Road, London, UK) to within 10% and 25%, respectively; and changes in pollutant concentrations of the right order of magnitude behind roadside barriers in a wind tunnel simulation of a street canyon and a real open-road environment. However, the model underestimates the benefits of a barrier in a simulated street canyon under perpendicular wind conditions. The prototype software is a first step towards informing practitioners of the site-specific impacts of vegetation barriers, which should always be additional to (i.e., no substitute for) essential emission reductions. The code is open-source to engage further researchers in its continued development.