Air Pollution Dispersion Model Research Articles

Impact assessment of spatial-temporal distribution of riverine dust on air quality using remote sensing data and numerical modeling.

Soil erosion is a severe problem in Taiwan due to the steep terrain, fragile geology, and extreme climatic events resulting from global warming. Due to the rapidly changing hydrological conditions affecting the locations and the amount of transported sand and fine particles, timely impact evaluation and riverine dust control are difficult, particularly when resources are limited. To comprehend the impact of desertification in estuarine areas on the variation of air pollutant concentrations, this study utilized remote sensing technology coupled with an air pollutant dispersion model to determine the unit contribution of potential pollution sources and quantify the effect of riverine dust on air quality. The images of the downstream area of the Beinan River basin captured by Formosat-2 in May 2006 were used to analyze land use and land cover (LULC) composition. Subsequently, the diffusion model ISCST-3 based on Gaussian distribution was utilized to simulate the transport of PM across the study area. Finally, a mixed-integer programming model was developed to optimize resource allocation for dust control. Results reveal that sand deposition in specific river sections significantly influences regional air quality, owing to the unique local topography and wind field conditions. The present optimal plan model for regional air quality control further showed that after implementing engineering measures including water cover, revegetation, armouring cover, and revegetation, total PM concentrations would be reduced by 51%. The contribution equivalent calculation, using the air pollution diffusion model, was effectively integrated into the optimization model to formulate a plan for reducing riverine dust with limited resources based on air quality requirements.

Syntrichia ruralis as a suitable bioindicator for urban areas – the case study of Tallinn city

Environmental pollution is one of the most important problems in urban environment. Mosses are good indicatorsof air pollution. In Estonia, heavy metals have been measured from Pleurozium schreberi and Hylocomium splendens, whichdo not grow in areas of Tallinn with a higher pollution load. In the present study, Cu, Fe and Cd were measured from fivemoss species growing in contaminated as well less polluted areas of Tallinn. Based on stationary and street pollution sourceinventory and air pollution dispersion modelling, the long-term average concentrations of fine particles (PM10) and nitrogenoxides (NOx) in air were estimated. The work revealed that it is possible to find a moss species that is common in Tallinnand grows in both polluted and less polluted areas – Syntrichia ruralis, which is the most suitable species for bioindicationbased on this work. Moss species Ceratodon purpureus accumulated the most Cd, Cu, and Fe, then Brachytheciumrutabulum/Sciuro-hypnum curtum, and Rhytidiadelphus squarrosus the least. Statistically significant higher Fe concentrationswere in the Syntrichia ruralis, compared to the Sciuro-hypnum curtum and Rhytidiadelphus squarrosus. The Syntrichia ruralisalso had significantly higher Cd content compared to the Brachythecium rutabulum/Sciuro-hypnum curtum. The results ofthe GLM analysis showed that the content of various heavy metals depends on the moss species and the degree of fine particlesin the environment, and it didn't depend on whether the moss grows on the soil or a hard substrate such as concrete, stoneor asphalt.

“No fences make bad neighbors” but markets make better ones: cap-and-trade reduces cross-border SO2 in a natural experiment

The clean air interstate rule (CAIR) was a regional cap-and-trade program announced in 2005 which covered 27 eastern US states and sought to reduce sulfur dioxide emissions from coal-fired power plants. The rule was later vacated after a court found that the non-targeted design of the program did not comply with the Clean Air Act provision to regulate interstate air pollution. Using a custom air pollution dispersion model, I calculate the interstate SO2 pollution from 493 coal-fired power plants across the United States between 1997 and 2020. In a difference-in-differences setup with plants not covered by CAIR in the control group, I estimate the treatment effect of the program on overall- and cross-border SO2 emissions and find a 24% reduction in overall emissions and reduces the risk that a plant violates air quality standards across state borders by 2–4%. I report evidence of heterogeneous treatment effects where the reduction in overall emissions attributed to CAIR is lower among plants transporting SO2 in excess of 1% of the National Air Quality Standards to another state.

Health impacts of PM2.5 originating from residential wood combustion in four nordic cities

BackgroundResidential wood combustion (RWC) is one of the largest sources of fine particles (PM2.5) in the Nordic cities. The current study aims to calculate the related health effects in four studied city areas in Sweden, Finland, Norway, and Denmark.MethodsHealth impact assessment (HIA) was employed as the methodology to quantify the health burden. Firstly, the RWC induced annual average PM2.5 concentrations from local sources were estimated with air pollution dispersion modelling. Secondly, the baseline mortality rates were retrieved from the national health registers. Thirdly, the concentration-response function from a previous epidemiological study was applied. For the health impact calculations, the WHO-developed tool AirQ + was used.ResultsAmongst the studied city areas, the local RWC induced PM2.5 concentration was lowest in the Helsinki Metropolitan Area (population-weighted annual average concentration 0.46 µg m− 3) and highest in Oslo (2.77 µg m− 3). Each year, particulate matter attributed to RWC caused around 19 premature deaths in Umeå (95% CI: 8–29), 85 in the Helsinki Metropolitan Area (95% CI: 35–129), 78 in Copenhagen (95% CI: 33–118), and 232 premature deaths in Oslo (95% CI: 97–346). The average loss of life years per premature death case was approximately ten years; however, in the whole population, this reflects on average a decrease in life expectancy by 0.25 (0.10–0.36) years. In terms of the relative contributions in cities, life expectancy will be decreased by 0.10 (95% CI: 0.05–0.16), 0.18 (95% CI: 0.07–0.28), 0.22 (95% CI: 0.09–0.33) and 0.63 (95% CI: 0.26–0.96) years in the Helsinki Metropolitan Area, Umeå, Copenhagen and Oslo respectively. The number of years of life lost was lowest in Umeå (172, 95% CI: 71–260) and highest in Oslo (2458, 95% CI: 1033–3669).ConclusionsAll four Nordic city areas have a substantial amount of domestic heating, and RWC is one of the most significant sources of PM2.5. This implicates a substantial predicted impact on public health in terms of premature mortality. Thus, several public health measures are needed to reduce the RWC emissions.

A community-integrated geographic information system study of air pollution exposure impacts in Colfax, LA

ABSTRACT A community-integrated geographic information systems (CIGIS) study assimilating qualitative and quantitative information about human exposures and health was conducted in Colfax, Louisiana, which hosts a commercial open burn/open detonation thermal treatment (TT) facility that destroys hazardous waste from Superfund sites, explosives, military ordnances, and propellants. Fifty-eight percent of residents identified as Black, and median annual income was $16,318, with 90% of the population living below the poverty line. We conducted oral history interviews of twenty-nine residents and mined public records to document the community’s experiences. Interviews focused on themes of Colfax’s history, changing community fabric, resident health, and air pollution. The oral histories and public comments by community members provided information about lived experiences, including several health conditions, toleration of noise and vibration, property damage, and resulting changes to activity levels. These statements provided insight into the extent of suffering experienced by the local community. We also ran dispersion models for dates in 2020 when the waste stream composition, mass, and burn/smoldering times were provided in the facility’s public records. The dispersion models placed the air pollution at the homes of residents during some of the time, and waste stream records from the TT facility agree with community testimony about health effects based on the known health effects of those compounds. CIGIS integration of our community-based qualitative data and maps with quantitative air pollution dispersion model output illustrated alignment between community complaints of impacts to health and property, known toxicological information about waste stream compounds, and dispersion model output.

Disperzija hemijskih sredstava u policijskoj praksi suzbijanja građanskih nereda

This study seeks to determine the spatial and temporal concentration of chemical agents (specifically - orthochlorobenzylidenemalononitrile - corson & stought (CS)) used by law enforcement in controlling civil disorders, depending on meteorological conditions and the time of the day. This study was conducted using the Gaussian air pollutant dispersion model. Based on findings of this study, conclusions pertaining to distance (in meters) and duration (in seconds) of the use of chemical agents under favorable, average, and unfavorable meteorological conditions, as well as conditions determined by the time of the day, both in urban and rural areas were drawn. Based on these conclusions, recommendations were made for the management and tactics of the engagement of police units in situations where the use of chemical agents occurs. Additionally, recommendations for future research in this area have been identified.

Air Pollution Dispersion Modelling using GRAL in Area Near Coal-Steam Power Plant at Central Java

Sulfur dioxide and Nitrogen dioxide were significant emissions emitted from coal-steam power plants that may cause health problems for humans and damage the environment. Studying the SO2 and NO2 gradients in Indonesian residential communities is critical for evaluating resident's SO2 and NO2 exposure. The method developed to assist analysis of spatial SO2 and NO2 gradients on a community scale combines a mesoscale Lagrangian dispersion model with field observations around coal-steam power plants using GRAL. The objectives of this study focused on GRAL dispersion of SO2 and NO2 in an Indonesian residential community near the coal-steam power plant, with a 6 km x 8 km resolution. Analysis of this model indicates a correlation between simulation and observation, with SO2 coefficient correlation (R) within 0.5 – 0.82 and NO2 coefficient correlation (R) within 0.30 – 0.59. Model performances analyze by NMSE and FB. The SO2 model is comparable to observation data since it has a better average NMSE and FB than the NO2 model. Due to data limitation of observation collected by grab sampling instead of continuous ambient measurement system affect different respond time compared with hourly data from the model.

A 3D mathematical evaluation of the emission into the air of reactive BTEX compounds: A new approach for mechanism reduction

BTEX species are abundant volatile organic compounds that are classified as main pollutants by several environmental protection agencies. In this study, a new 3D air pollution dispersion model with the capability of taking into account the BTEX's reactions was developed and evaluated. The Toxchem model is employed to estimate the amount of emitted BTEX from several area sources, followed by simulating the transport of the reactive species through a 3D model. Also, reduced mechanisms were developed, based on Master Chemical Mechanism (MCM), for the simulation of the atmospheric chemistry of BTEX. The application of the mechanism reduction method yielded a mechanism of 43 species and 45 reactions. Based on findings, the deviation of the reduced mechanism from the whole mechanism was < 4% throughout the simulation. In addition, the result showed that, during a 12 h period of simulation, the effect of the atmospheric chemical reaction on reducing the final concentration of benzene, toluene, ethylbenzene, m-xylene, o-xylene, and p-xylene was about 0.3, 1, 1.4, 5, 3.1, and 3.2%, respectively. Lastly, it was indicated that the estimated emission rates by Toxchem and simulated concentrations by the dispersion model were in good agreement with the reported experimental values.

Air pollution dispersion modeling of runway and apron at Sam Ratulangi international airport

Air pollution generated from airport activities has become public concern and the subject of more rigorous government regulations.  The Airport Operators are stipulated to control the pollution and for the accountability of air quality that might affect public health. The main objective of this study is to establish a model for the distribution of air pollutants and to predict their concentrations generated by the runway and apron operations at Sam Ratulangi International Airport (Manado) until 2024, in accordance with the airport expansion program. The data was collected in the airport surrounding area in 2018, while the climate data over a span of 10 years, from 2009 to 2018, was obtained from Sam Ratulangi Meteorological Station. The modeling on dispersion of air pollutant gases was developed by the Gaussian Plume Equation. The simulation was performed using AERMOD software, and the results visualized by GIS software. AERMOD software was recommended by the US-EPA to predict the impact of air pollutants. The results predicted that the maximum concentrations of NOx; HC; and CO generated by runway activities modeling in 2024 were 250 μg.m-3; 6.4 μg.m-3; and 87 μg.m-3 respectively. The results also predicted that the maximum concentrations of NOx; CO; and PM10 due to apron operational activities in 2024 were 260 μg.m-3; 892 μg.m-3; and 2.5 μg.m-3 respectively. The model predicted that in 2024 the air pollution at Sam Ratulangi International Airport will remain under the limit as defined in Indonesian Government Regulation No. 22 of 2021. To mitigate the future increase in air emissions due to the increase in airport capacity, the recommendation were proposed in the several areas, which were including operation management, technology, policies and airport regulations, as well as the provision of green area.

Assessment of Respiratory morbidity burden from exposure to vegetation fire-PM2.5 in Upper Northern Thailand for the year 2018

BACKGROUND AND AIM: The seasonal occurrence of vegetation fire in the Mainland Southeast Asia (MSEA) has deteriorated air quality, which can increase respiratory diseases. Nevertheless, no study has assessed the health burden from exposure to vegetation-fire smoke in the MSEA. We aimed to estimate the number of hospital visits from respiratory diseases attributable to vegetation-fire PM2.5 in the Upper northern Thailand (UNT). METHODS: The modelled vegetation fire-PM2.5 were obtained from System for Integrated modeLling of Atmosheric coMposition (SILAM) model, which is a global-to-meso-scale air pollution dispersion model. Vegetation fire-PM2.5 were simulated based on fire radiative power data of Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data. We averaged the extracted grid-cell of fire-PM2.5 within district boundaries. The number of respiratory disease-related hospital visits attributable to fire-PM2.5 was estimated for the whole year and burning period (January to April) of the year 2018 by using the modelled daily fire-PM2.5, population data, and concentration-response function derived from the previous epidemiological study. RESULTS:In 2018, we estimated 17,817 hospital visits for respiratory diseases attributable to vegetation fire-PM2.5 in the UNT. Our estimation was accounted approximately 1% of the total hospital visits for respiratory diseases throughout the year and 3 % during the burning season. CONCLUSIONS:This study suggests that PM2.5 emitted from vegetation fire events poses a health burden across the UNT. KEYWORDS: Health burden estimation, Vegetation fires, Hospital visits, Respiratory diseases

Atmospheric transport over open-pit mines: The effects of thermal stability and mine depth

A key difficulty in air pollution dispersion modeling and quantifying fugitive emission fluxes of pollutants from open-pit mines is that the meteorological fields for such complex terrains cannot be reliably predicted using simplistic surface layer theory. In this study, transport phenomena over a shallow (100 m) and a deep (500 m) synthetic mine are predicted under thermally unstable, neutral, and stable conditions using CFD modelling. The skimming flow is only predicted under the neutral case, while more complex flow patterns emerge otherwise. Under the unstable case, the shallow and deep mines induce enhanced mixing downstream of the mine, resulting in substantial vertical plume transport and dilution of the pollutants released from the mine. Under the stable case, the plume from the shallow mine is restricted to the surface layer downstream of the mine. However, under the stable case, the plume from the deep mine rises into the substantial portion of the boundary layer due to formation of a standing wave over and inside the mine. The results suggest that the CFD model can predict transport phenomena over open-pit mines reliably, so that the meteorological fields may be incorporated in operational models to improve accuracy of their predictions. • CFD is used to study atmospheric transport phenomena for open-pit mines under different mine depth and thermal stability conditions. • Skimming flow occurs for the neutral case, while enhanced mixing of passive scalar, released from the mine surface, occurs for the unstable case. • Under stable condition, a shallow surface layer is predicted for the shallow mine, while a deep standing wave is predicted for the deep mine. • Such accurate meteorological predictions can be incorporated in operational air quality and emission flux models for open-pit mines.

WRF4PALM v1.0: a mesoscale dynamical driver for the microscale PALM model system 6.0

Abstract. A set of Python-based tools, WRF4PALM, has been developed for offline nesting of the PALM model system 6.0 into the Weather Research and Forecasting (WRF) modelling system. Time-dependent boundary conditions of the atmosphere are critical for accurate representation of microscale meteorological dynamics in high-resolution real-data simulations. WRF4PALM generates initial and boundary conditions from WRF outputs to provide time-varying meteorological forcing for PALM. The WRF model has been used across the atmospheric science community for a broad range of multidisciplinary applications. The PALM model system 6.0 is a turbulence-resolving large-eddy simulation model with an additional Reynolds-averaged Navier–Stokes (RANS) mode for atmospheric and oceanic boundary layer studies at microscale (Maronga et al., 2020). Currently PALM has the capability to ingest output from the regional scale Consortium for Small-scale Modelling (COSMO) atmospheric prediction model. However, COSMO is not an open source model and requires a licence agreement for operational use or academic research (http://www.cosmo-model.org/, last access: 23 April 2021). This paper describes and validates the new free and open-source WRF4PALM tools (available at https://github.com/dongqi-DQ/WRF4PALM, last access: 23 April 2021). Two case studies using WRF4PALM are presented for Christchurch, New Zealand, which demonstrate successful PALM simulations driven by meteorological forcing from WRF outputs. The WRF4PALM tools presented here can potentially be used for micro- and mesoscale studies worldwide, for example in boundary layer studies, air pollution dispersion modelling, wildfire emissions and spread, urban weather forecasting, and agricultural meteorology.

Modeling and numerical experiments of air pollution on a complex terrain.

The modeling of air contaminant dispersion extended from the Gaussian plume model is proposed. This model takes onto account the transport of pollutants emitted from open biomass burning located on a complex terrain where the concentration behavior is modeled by the advection-diffusion equation in a stationary frame. In addition, the existence, uniqueness, and regularity of this problem are discussed. The discontinuous Galerkin method is employed for numerical treatments. Especially for numerical experiments, the propagation of air pollutants in the complex terrain areas in Chiang Mai province, Thailand, is investigated. Results of numerical experiments demonstrate the validity of the air pollution dispersion model.

A hybrid air pollution / land use regression model for predicting air pollution concentrations in Durban, South Africa.

The objective of this paper was to incorporate source-meteorological interaction information from two commonly employed atmospheric dispersion models into the land use regression technique for predicting ambient nitrogen dioxide (NO2), sulphur dioxide (SO2), and particulate matter (PM10). The study was undertaken across two regions in Durban, South Africa, one with a high industrial profile and a nearby harbour, and the other with a primarily commercial and residential profile. Multiple hybrid models were developed by integrating air pollution dispersion modelling predictions for source specific NO2, SO2, and PM10 concentrations into LUR models following the European Study of Cohorts for Air Pollution Effects (ESCAPE) methodology to characterise exposure, in Durban. Industrial point sources, ship emissions, domestic fuel burning, and vehicle emissions were key emission sources. Standard linear regression was used to develop annual, summer and winter hybrid models to predict air pollutant concentrations. Higher levels of NO2 and SO2 were predicted in south Durban as compared to north Durban as these are industrial related pollutants. Slightly higher levels of PM10 were predicted in north Durban as compared to south Durban and can be attributed to either traffic, bush burning or domestic fuel burning. The hybrid NO2 models for annual, summer and winter explained 60%, 58% and 63%, respectively, of the variance with traffic, population and harbour being identified as important predictors. The SO2 models were less robust with lower R2 annual (44%), summer (53%) and winter (46%), in which industrial and traffic variables emerged as important predictors. The R2 for PM10 models ranged from 80% to 85% with population and urban land use type emerging as predictor variables.

Exposure to particulate matter and risk of conversion from mild cognitive impairment to dementia: a cohort study in a Northern Italy population

Background/Aim Exposure to air pollutants such as inhalable particulate matter has been linked to increased risk of chronic disease including neurodegenerative diseases, such as Alzheimer’s dementia. In this study, we aimed to evaluate the effect of long-term exposure to outdoor air pollution, and specifically to particulate matter ≤10 μm (PM10), on the risk of dementia in a cohort of subjects with mild cognitive impairment. MethodsWe recruited 53 subjects newly-diagnosed with mild cognitive impairment of non-vascular origin and residing in the Modena and Reggio Emilia provinces of Northern Italy. Using a Geographical Information System and a validated air pollution dispersion model, we assessed exposure to outdoor PM10 from motorized traffic at subjects’ residence. We investigated the relation of these concentrations to subsequent onset of dementia, using a Cox proportional hazards model. We computed hazard ratio (HR) and 95% confidence interval (CI) according to fixed categories of PM10 exposure, adjusting for sex, age, and educational attainment level. ResultsDuring a median follow-up of 42 months, 19 participants developed Alzheimer’s dementia, 3 frontotemporal dementia and 2 Lewy body dementia. Baseline PM10 exposure levels were 9.6 µg/m3 on average. Using PM10 levels below 5 µg/m3 as reference, we found a dose-response increase in any dementia risk with HR of 1.04 (95% CI 0.41-2.66) at 5-10 µg/m3, 1.32 (95% CI 0.36-4.92) at 10-20 µg/m3, and 1.38 (95% CI 0.14-13.13) above 20 µg/m3, respectively. Conclusions Our results suggest that exposure to particulate matter emitted by motorized vehicles increases the risk of conversion from mild cognitive impairment to dementia, though the low number of study participants suggests caution in the interpretation of these findings.

Long-term Exposure to Low Concentration of PM2.5 and Mortality: A Danish Nurse Cohort Study

Background/Aim. The association between air pollution and mortality is well established, yet studies in areas with low levels of air pollution, below EU limit values, with adjustment for road traffic noise, are sparse. We examined the association of long-term exposure to fine particulate matter (PM2.5) with mortality due to cardiovascular disease (CVD), respiratory disease (RD), and diabetes as well as all-cause natural mortality.Methods. We used data on 24,541 female nurses from the Danish Nurse Cohort who, at recruitment in 1993 or 1999, reported information on risk factors. Data on death were obtained from the Danish Register of Causes of Death until the end of 2013. Annual mean concentrations of PM2.5 in 1990-2013 at the nurses’ residences were estimated using the Danish air pollution dispersion modeling system. We examined associations between the 3-year running mean of PM2.5 with all-cause natural and cause-specific mortality by using time-varying Cox Regression models, adjusting for individual characteristics.Results. During the study time-period, 3,708 nurses died, and of these, 843 were due to CVD, 310 due to RD, and 64 due to diabetes. The mean level of PM2.5 at the baseline was 20.5 µg/m3. In the fully adjusted models, including road traffic noise, we found positive associations of 3-year running mean of PM2.5 with all-cause natural (hazard ratio; 95% confidence interval: 1.06; 1.01-1.11), CVD (1.14; 1.03-1.26), and diabetes mortality (1.41; 1.05-1.90), per interquartile range of 4.39 μg/m3. In subjects exposed to low levels of PM2.5 (< 20 µg/m3), we found even stronger association with all-cause natural (1.19; 1.11-1.27), CVD (1.27; 1.01-1.46), RD (1.27; 1.00-1.60), and diabetes mortality (1.44; 0.83-2.48).Conclusion. In a Danish cohort of female nurses, long-term exposures to low-levels of PM2.5 were associated with all-cause mortality, strongest for diabetes, and CVD, which persisted at the levels below the current EU limit values.

Air Pollution Dispersion Modeling in Port Areas

For the last couple of decades, environmental protection awareness within port areas is gaining ever more importance. Ports can have a tremendous impact on the environment, especially in terms of air pollution. The main pollution sources are various port activities such as road and rail traffic, cargo handling and marine vessel operations. Air quality models can be of great help in estimating the effect on the ambient air quality from one or more sources emitting pollutants to the atmosphere. One of those models is the widely used Gaussian Plume dispersion approach. Based on existing measurements and port activity data, models can simulate the dispersion of air pollutants caused by activities and operations taking place within the port. By using historical data, they can simulate the current state of the air quality in the port and with the help of weather predictions simulate possible future situation. Simulations can assist the port manager/operator in the decision-making process in order to optimize various activities within the port and minimize their impact on the environment. One of the main objectives of the Horizon 2020 Project PIXEL (Port IoT for environmental leverage) is the deployment of environmental pollution models which can aid in the decision-making processes within the port domain. This paper reviews the current advances in the field of air pollution modelling with a special emphasis on port scenarios.

Model-based dense air pollution maps from sparse sensing in multi-source scenarios

A method for producing dense air pollution maps, based on any given air-pollution dispersion model, is presented. The scheme consists of two phases. At the first stage, sources' locations and emission rates, i.e., source term estimation, as a function of the model's parameter space are sought (“backward computation”). Then, the source term is used to generate the dense maps utilizing the same dispersion model (“forward computation”). The algorithm is model-invariant to the dispersion model, and thus is suitable for a wide range of applications according to the required accuracy and available resources. A simulation of an industrial area demonstrated that this method produced more accurate maps than current state-of-the-art techniques. The resulting dense air pollution map is thus a valuable tool for air pollution mitigation, regulation and research.

High-resolution simulation of local traffic-related NOx dispersion and distribution in a complex urban terrain

Urban air pollution features large spatial and temporal variations due to the high heterogeneity in emissions and ventilation conditions, which render the pollutant distributions in complex urban terrains difficult to measure. Current urban air pollution models are not able to simulate pollutant dispersion and distribution at a low computational cost and high resolution. To address this limitation, we have developed the urban terrain air pollution (UTAP) dispersion model to investigate, at a spatial resolution of 5 m and a temporal resolution of 1 h, the distribution of the local traffic-related NOx concentration at the pedestrian level in a 1 × 1 km2 area in Baoding, Hebei, China. The UTAP model was shown to be capable of capturing the local pollution variations in a complex urban terrain at a low computational cost. We found that the local traffic-related NOx concentration along or near major roads (10–200 μg m−3) was 1–2 orders of magnitude higher than that in places far from roads (0.1–10 μg m−3). Considering the background pollution, the NO and NO2 concentrations exhibited similar patterns with higher concentrations in street canyons and lower concentrations away from streets, while the O3 concentration exhibited the opposite behavior. Sixty percent of the NOx concentration likely stemmed from local traffic when the background pollution level was low. Both the background wind speed and direction substantially impacted the overall pollution level and concentration variations, with a low wind speed and direction perpendicular to the axes of most streets identified as unfavorable pollutant dispersion conditions. Our results revealed a large variability in the local traffic-related air pollutant concentration at the pedestrian level in the complex urban terrain, indicating that high-resolution computationally efficient models such as the UTAP model are required to accurately estimate the pollutant exposure of urban residents.

Street-scale air quality modelling for Beijing during a winter 2016 measurement campaign

Abstract. We examine the street-scale variation of NOx, NO2, O3 and PM2.5 concentrations in Beijing during the Atmospheric Pollution and Human Health in a Chinese Megacity (APHH-China) winter measurement campaign in November–December 2016. Simulations are performed using the urban air pollution dispersion and chemistry model ADMS-Urban and an explicit network of road source emissions. Two versions of the gridded Multi-resolution Emission Inventory for China (MEIC v1.3) are used: the standard MEIC v1.3 emissions and an optimised version, both at 3 km resolution. We construct a new traffic emissions inventory by apportioning the transport sector onto a detailed spatial road map. Agreement between mean simulated and measured pollutant concentrations from Beijing's air quality monitoring network and the Institute of Atmospheric Physics (IAP) field site is improved when using the optimised emissions inventory. The inclusion of fast NOx–O3 chemistry and explicit traffic emissions enables the sharp concentration gradients adjacent to major roads to be resolved with the model. However, NO2 concentrations are overestimated close to roads, likely due to the assumption of uniform traffic activity across the study domain. Differences between measured and simulated diurnal NO2 cycles suggest that an additional evening NOx emission source, likely related to heavy-duty diesel trucks, is not fully accounted for in the emissions inventory. Overestimates in simulated early evening NO2 are reduced by delaying the formation of stable boundary layer conditions in the model to replicate Beijing's urban heat island. The simulated campaign period mean PM2.5 concentration range across the monitoring network (∼15 µg m−3) is much lower than the measured range (∼40 µg m−3). This is likely a consequence of insufficient PM2.5 emissions and spatial variability, neglect of explicit point sources, and assumption of a homogeneous background PM2.5 level. Sensitivity studies highlight that the use of explicit road source emissions, modified diurnal emission profiles, and inclusion of urban heat island effects permit closer agreement between simulated and measured NO2 concentrations. This work lays the foundations for future studies of human exposure to ambient air pollution across complex urban areas, with the APHH-China campaign measurements providing a valuable means of evaluating the impact of key processes on street-scale air quality.