Air Quality Modeling Studies Research Articles

An Investigation on the use of air quality models in ship emission forecasts

Today's world trade operates on the basis of maritime transportation. Combating the environmental impacts of maritime transportation has become a global concern. MARPOL Annex VI contains rules for the prevention of air pollution from ships. Air pollution represents a more abstract pollution than other annexes of MARPOL. In this context, the first and most critical step is to measure air pollution. Nowadays it is possible to calculate ship emissions with individual ship activity data instead of the traditional, fuel-based approach. One of the most ideal data sources for this calculation is AIS data. The critical question to be answered in the literature is how to obtain the highest resolution output using AIS data. Here air quality modelling gains strategic importance. In this study, air quality models used in ship emission calculations were examined with the PRISMA method and the most commonly used Eulerian and Lagrangian models were discussed. For this purpose, studies using AIS data in ship emission calculations were separated through the Scopus database and air quality modelling studies were filtered. Thus, the profile of air quality models used in ship-borne air pollution studies in the literature was obtained and a methodological reference source was created for future studies.

Current status of model predictions of volatile organic compounds and impacts on surface ozone predictions during summer in China

Abstract. Volatile organic compounds (VOCs) play a crucial role in the formation of tropospheric ozone (O3) and secondary organic aerosols. VOC emissions are generally considered to have larger uncertainties compared to other pollutants, such as sulfur dioxide and fine particulate matter (PM2.5). Although predictions of O3 and PM2.5 have been extensively evaluated in air quality modeling studies, there has been limited reporting on the evaluation of VOCs, mainly due to a lack of routine VOC measurements at multiple sites. In this study, we utilized VOC measurements from the “Towards an Air Toxic Management System in China” (ATMSYC) project at 28 sites across China and assessed the predicted VOC concentrations using the Community Multiscale Air Quality (CMAQ) model with the widely used Multi-resolution Emission Inventory for China (MEIC). The ratio of predicted to observed total VOCs was found to be 0.74 ± 0.40, with underpredictions ranging from 2.05 to 50.61 ppbv (5.77 % to 85.40 %) at 24 sites. A greater bias in VOC predictions was observed in industrial cities in the north and southwest, such as Jinan, Shijiazhuang, Lanzhou, Chengdu, and Guiyang. In terms of different VOC components, alkanes, alkenes, non-naphthalene aromatics (ARO2MN), alkynes, and formaldehyde (HCHO) had prediction-to-observation ratios of 0.53 ± 0.38, 0.51 ± 0.48, 0.31 ± 0.38, 0.41 ± 0.47, and 1.21 ± 1.61, respectively. Sensitivity experiments were conducted to assess the impact of the VOC prediction bias on O3 predictions. While emission adjustments improved the model performance for VOCs, resulting in a change in the ratio of total VOCs to 0.86 ± 0.47, they also exacerbated O3 overprediction relative to the base case by 0.62 % to 6.27 % across the sites. This study demonstrates that current modeling setups and emission inventories are likely to underpredict VOC concentrations, and this underprediction of VOCs contributes to lower O3 predictions in China.

Utility of Geostationary Lightning Mapper-derived lightning NO emission estimates in air quality modeling studies

Abstract. Lightning is one of the primary natural sources of nitric oxide (NO), and the influence of lightning-induced NO (LNO) emission on air quality has been investigated in the past few decades. In the current study an LNO emissions model, which derives LNO emission estimates from satellite-observed lightning optical energy, is introduced. The estimated LNO emission is employed in an air quality modeling system to investigate the potential influence of LNO on tropospheric ozone. Results show that lightning produced 0.174 Tg N of nitrogen oxides (NOx = NO + NO2) over the contiguous US (CONUS) domain between June and September 2019, which accounts for 11.4 % of the total NOx emission. In August 2019, LNO emission increased ozone concentration within the troposphere by an average of 1 %–2 % (or 0.3–1.5 ppbv), depending on the altitude; the enhancement is maximum at ∼ 4 km above ground level and minimum near the surface. The southeastern US has the most significant ground-level ozone increase, with up to 1 ppbv (or 2 % of the mean observed value) difference for the maximum daily 8 h average (MDA8) ozone. These numbers are near the lower bound of the uncertainty range given in previous studies. The decreasing trend in anthropogenic NOx emissions over the past 2 decades increases the relative contribution of LNO emissions to total NOx emissions, suggesting that the LNO production rate used in this study may need to be increased. Corrections for the sensor flash detection efficiency may also be helpful. Moreover, the episodic impact of LNO on tropospheric ozone can be considerable. Performing backward trajectory analyses revealed two main reasons for significant ozone increases: long-distance chemical transport and lightning activity in the upwind direction shortly before the event.

Turbulent characteristics in complex coastal areas assessed using BSWO observations and WRF-LES simulation results

Studies exploring the turbulent structure of the planetary boundary layer (PBL) over complex topographies are very limited, especially at night, because of the lack of available observations and difficulty of highly resolved simulation. In this study, micro-meteorological and turbulent characteristics over complex coastal areas were investigated based on high spatial-temporal resolution by using the Weather Research and Forecasting–Large Eddy Simulation (WRF-LES) model. Then, the simulations were compared to comprehensive observations obtained at the Boseong Standard Weather Observatory (BSWO), which is equipped with various observation facilities, such as a meteorological observation tower with a height of 307 m, and aiding ground-based remote sensing measurements used for PBL research. The comparison between the WRF-LES model results and the measurements by the wind lidar system at the BSWO showed that the LES approach in this study reproduced the spatial/vertical wind field structure and land-sea wind circulation distinctly better than the conventional PBL schemes. High turbulent kinetic energy (TKE) was observed near the surface level at night due to the increased nocturnal shear production following the evolution of land and mountain breezes. The calculated TKE from WRF-LES model was applied to identify the mixing intensity based on the mixing length and eddy diffusion coefficient within the boundary layer, and then those were evaluated with the observed extinction coefficient from the aerosol Micro-Pulse Lidar system (MPL). In addition, the planetary boundary layer height (PBLH) at night was explored by using the TKE threshold method combined with the bulk Richardson number threshold method for the daytime PBLH. Compared with the observed value from a ceilometer at the BSWO, we found that the TKE threshold method proposed in this study could yield highly accurate PBLH values, especially at night. It is also expected that the enhanced application of WRF-LES like to this study can lead to understanding and prediction with the high confidence for much detail and accurate spatiotemporal turbulent structure over complex coastal areas, which will contribute to more comprehensive air quality modeling studies.

New processing methodology to incorporate marine halocarbons and dimethyl sulfide (DMS) emissions from the CAMS-GLOB-OCE dataset in air quality modeling studies

New processing methodology to incorporate marine halocarbons and dimethyl sulfide (DMS) emissions from the CAMS-GLOB-OCE dataset in air quality modeling studies

An Air Quality Modeling and Disability-Adjusted Life Years (DALY) Risk Assessment Case Study: Comparing Statistical and Machine Learning Approaches for PM2.5 Forecasting

Despite Central and Northern Asia having several cities sharing a similar harsh climate and grave air quality concerns, studies on air pollution modeling in these regions are limited. For the first time, the present study uses multiple linear regression (MLR) and a random forest (RF) algorithm to predict PM2.5 concentrations in Astana, Kazakhstan during heating and non-heating periods (predictive variables: air pollutant concentrations, meteorological parameters). Estimated PM2.5 was then used for Disability-Adjusted Life Years (DALY) risk assessment. The RF model showed higher accuracy than the MLR model (R2 from 0.79 to 0.98 in RF). MLR yielded more conservative predictions, making it more suitable for use with a lower number of predictor variables. PM10 and carbon monoxide concentrations contributed most to the PM2.5 prediction (both models), whereas meteorological parameters showed lower association. Estimated DALY for Astana’s population (2019) ranged from 2160 to 7531 years. The developed methodology is applicable to locations with comparable air pollution and climate characteristics. Its output would be helpful to policymakers and health professionals in developing effective air pollution mitigation strategies aiming to mitigate human exposure to ambient air pollutants.

Comparison of two air quality models in complex terrain near sea shore

Air pollution is the most important environmental problem in Zonguldak, Turkey due to excessive coal combustion and thermal power plant emissions. The city center is located on a complex terrain near the Black Sea shore. There exist some previous studies about PM10 pollution in the study area, but none of them is related to the spatial distribution of the pollutant. This air quality modeling study aims to fill this gap in the literature. Firstly, PM10 emission inventory has been prepared for point, line, and area sources for the year 2011. For that period, bituminous coal was the principal fuel for domestic heating in houses and generating electricity in thermal power plants, therefore particulate matter (PM10) was the leading air pollutant. Emission inventory calculations revealed that 2710.2 tons of PM10 have been emitted to the atmosphere from all sources in the study area. Then, the air quality modeling has been performed for PM10 by using two air quality models: AERMOD and CALPUFF. According to the modeling results, the PM10 pollution levels may pose a health threat to the inhabitants of Zonguldak. The maximum PM10 concentrations predicted by the CALPUFF model were higher than that of the AERMOD model. The model predicted values plus background concentration were validated against the PM10 measurements by using fractional bias, index of agreement, geometric mean bias, and geometric mean-variance. According to the model performance analysis, CALPUFF showed slightly better performance as compared to AERMOD.

Realistic operation of two residential cordwood-fired outdoor hydronic heater appliances—Part 3: Optical properties of black and brown carbon emissions

ABSTRACT Residential biomass combustion is a source of carbonaceous aerosol. Inefficient combustion, particularly of solid fuels produces large quantities of black and brown carbon (BC and BrC). These particle types are important as they have noted effects on climate forcing and human health. One method of measuring these quantities is by measurement of aerosol light-absorption and scattering, which can be performed using an aethalometer and nephelometer, respectively. These instruments are widely deployed in the study of ambient air and are frequently used in air quality modeling and source apportionment studies. In this study, we will describe (1) a method for measuring primary BC and BrC emissions from two residential log-fired wood hydronic heaters and (2) the BC and BrC emission from these devices over a wide range of operating conditions, such as cold-starts, warm-starts, four different levels of output ranging from 15% to 100% maximum rated output, and periods of repeated cycling. The range in flue-gas BC concentrations, measured using an aethalometer at the 880 nanometer (nm) wavelength, were between 5.09 × 102 and 2.24 × 104 micrograms per cubic meter (µg/m3) while the scattering coefficient of the flue-gas, measured by a nephelometer at 880 nm, ranged between 2.20 × 103 and 8.56 × 105 inverse megameters (Mm−1). The BrC concentrations, measured using the 370 nm wavelength of an aethalometer, were between 9.10 × 101 and 3.56 × 104 µg/m3. The calculated Angstrom Absorption Exponent (AAE) of the flue-gas aerosol ranged between 1.54 and 3.63. Performing a comparison between the measured BC concentration and an external particulate matter (PM) concentration showed that overall BC makes up roughly a quarter of the PM emitted by either of the two appliances. Further for both appliances, the cold-start and the test phase immediately following it had the highest BC and BrC concentrations, the highest measured scattering coefficient, as well as a low AAE. Implications: In this work we provide information on the black and brown carbon emissions from outdoor cordwood-fired hydronic heaters. Aethalometer based black carbon measurements are common in atmospheric science, but are uncommonly used in laboratory studies. This work helps to bridge that gap. This data helps to inform the work of modelers and policy makers interested in hydronic heaters and source apportioning biomass combustion emissions.

European primary emissions of criteria pollutants and greenhouse gases in 2020 modulated by the COVID-19 pandemic disruptions

Abstract. We present a European dataset of daily sector-, pollutant- and country-dependent emission adjustment factors associated with the COVID-19 mobility restrictions for the year 2020. We considered metrics traditionally used to estimate emissions, such as energy statistics or traffic counts, as well as information derived from new mobility indicators and machine learning techniques. The resulting dataset covers a total of nine emission sectors, including road transport, the energy industry, the manufacturing industry, residential and commercial combustion, aviation, shipping, off-road transport, use of solvents, and fugitive emissions from transportation and distribution of fossil fuels. The dataset was produced to be combined with the Copernicus CAMS-REG_v5.1 2020 business-as-usual (BAU) inventory, which provides high-resolution (0.1∘×0.05∘) emission estimates for 2020 omitting the impact of the COVID-19 restrictions. The combination of both datasets allows quantifying spatially and temporally resolved reductions in primary emissions from both criteria pollutants (NOx, SO2, non-methane volatile organic compounds – NMVOCs, NH3, CO, PM10 and PM2.5) and greenhouse gases (CO2 fossil fuel, CO2 biofuel and CH4), as well as assessing the contribution of each emission sector and European country to the overall emission changes. Estimated overall emission changes in 2020 relative to BAU emissions were as follows: −10.5 % for NOx (−602 kt), −7.8 % (−260.2 Mt) for CO2 from fossil fuels, −4.7 % (−808.5 kt) for CO, −4.6 % (−80 kt) for SO2, −3.3 % (−19.1 Mt) for CO2 from biofuels, −3.0 % (−56.3 kt) for PM10, −2.5 % (−173.3 kt) for NMVOCs, −2.1 % (−24.3 kt) for PM2.5, −0.9 % (−156.1 kt) for CH4 and −0.2 % (−8.6 kt) for NH3. The most pronounced drop in emissions occurred in April (up to −32.8 % on average for NOx) when mobility restrictions were at their maxima. The emission reductions during the second epidemic wave between October and December were 3 to 4 times lower than those occurred during the spring lockdown, as mobility restrictions were generally softer (e.g. curfews, limited social gatherings). Italy, France, Spain, the United Kingdom and Germany were, together, the largest contributors to the total EU27 + UK (27 member states of the European Union and the UK) absolute emission decreases. At the sectoral level, the largest emission declines were found for aviation (−51 % to −56 %), followed by road transport (−15.5 % to −18.8 %), the latter being the main driver of the estimated reductions for the majority of pollutants. The collection of COVID-19 emission adjustment factors (https://doi.org/10.24380/k966-3957, Guevara et al., 2022) and the CAMS-REG_v5.1 2020 BAU gridded inventory (https://doi.org/10.24380/eptm-kn40, Kuenen et al., 2022b) have been produced in support of air quality modelling studies.

Evaluation of Long-Term Modeling Fine Particulate Matter and Ozone in China During 2013–2019

Air quality in China has been undergoing significant changes due to the implementation of extensive emission control measures since 2013. Many observational and modeling studies investigated the formation mechanisms of fine particulate matter (PM2.5) and ozone (O3) pollution in the major regions of China. To improve understanding of the driving forces for the changes in PM2.5 and O3 in China, a nationwide air quality modeling study was conducted from 2013 to 2019 using the Weather Research and Forecasting/Community Multiscale Air Quality (WRF/CMAQ) modeling system. In this study, the model predictions were evaluated using the observation data for the key pollutants including O3, sulfur dioxide (SO2), nitrogen dioxide (NO2), and PM2.5 and its major components. The evaluation mainly focused on five major regions, that is , the North China Plain (NCP), the Yangtze River Delta (YRD), the Pearl River Delta (PRD), the Chengyu Basin (CY), and the Fenwei Plain (FW). The CMAQ model successfully reproduced the air pollutants in all the regions with model performance indices meeting the suggested benchmarks. However, over-prediction of PM2.5 was noted in CY. NO2, O3, and PM2.5 were well simulated in the north compared to the south. Nitrate (NO3−) and ammonium (NH4+) were the most important PM2.5 components in heavily polluted regions. For the performance on different pollution levels, the model generally over-predicted the clean days but underpredicted the polluted days. O3 was found increasing each year, while other pollutants gradually reduced during 2013–2019 across the five regions. In all of the regions except PRD (all seasons) and YRD (spring and summer), the correlations between PM2.5 and O3 were negative during all four seasons. Low-to-medium correlations were noted between the simulated PM2.5 and NO2, while strong and positive correlations were established between PM2.5 and SO2 during all four seasons across the five regions. This study validates the ability of the CMAQ model in simulating air pollution in China over a long period and provides insights for designing effective emission control strategies across China.

A comprehensive high-resolution gridded emission inventory of anthropogenic sources of air pollutants in Indian megacity Kolkata

In this study, we present a first-ever effort made to develop an ultra-high-resolution gridded emission inventory (i.e. ~ 0.4 km × ~ 0.4 km) for the Indian megacity Kolkata. As the rising demand for fossil fuels based energy along with the spread of urban corridors have forced the anthropogenic activities to a mounting level, therefore determining the sources responsible is of paramount importance. This has worsened not only the regional air quality but also has an indirect effect on the global air quality. The spatial and temporal variation of the source requires an accurate estimation of the surface emission which is the most essential parameter to study the air quality, that positively has been fulfilled in this study. The annual emission for 2020 is calculated to be 37.2 Gg/yr of PM2.5, 61.4 Gg/yr of PM10, 222.6 Gg/yr of CO, 131.3 Gg/yr of NOx, 60.3 Gg/yr of SO2, 120.4 Gg/yr of VOC, 9.5 Gg/yr of BC and 16.8 Gg/yr of OC that prevails in the toxic air of megacity Kolkata. The present surface chemistry dataset will be the first line of detailed information regarding emission hotspots in the megacity that could be used as important tool for clean air mitigation strategies, input into the air quality modeling study to tackle environmental issues, and public health.Article HighlightsIdentification of sources of pollutants through spatially resolved high-resolution inventory in megacity Kolkata.Unlike transport and industrial sector, municipal solid waste burning has emerged as another big source.Present information is vital for policy making to mitigate air quality issues and modeling studies.

A method to dynamically constrain black carbon aerosol sources with online monitored potassium

The result of Aethalometer model to black carbon (BC) source apportionment is highly determined by the absorption Ångström exponent (α) of aerosols from fossil fuel combustion (αff) and wood burning (αwb). A method using hourly measured potassium to calculate the αff and αwb values was developed in this study. Results showed that the optimal αff and αwb were 1.09 and 1.79 for the whole dataset. The optimal α values in the diurnal resolution were also calculated with αff and αwb varied in 1.02 –1.19 and 1.71–1.90, respectively. Using the dynamic α values, the Pearson correlation coefficient between BC and potassium from wood burning substantially improved compared to the results derived from the fixed α values. The method developed in this study is expected to provide more reasonable BC source identification results, which are helpful for air quality, climate, and human health modeling studies.

Processing methodology of global anthropogenic emissions for air quality modeling

The Global Emissions Initiative (GEIA) stores and offers global datasets of emission inventories developed in the last 30 years. One of the most recently updated global datasets covering anthropogenic source emissions is the Copernicus Atmosphere Monitoring Service (CAMS). This study applied NetCDF Command Operator (NCO) software to preprocess the anthropogenic sources included in the CAMS datasets and converted those files as an input in the Sparse Matrix Operator Kerner Emissions (SMOKE) model for future air quality modeling. As a result, six steps were applied to obtain the required file format. The case of the central coast in Chile was analyzed to compare the global database and official reports for the on-road transport sector. As a result, some differences were shown in the most populated locations of the domain of analysis. The rest of the zones registered similar values. The methodology exposed in this report could be applied in any other region of the planet for air quality modeling studies. The development of global datasets such as CAMS is useful for hemispheric analysis and could bring an estimation on the mesoscale. It represents an opportunity for those locations without official reports of non-updated data.•This study applied NCO commands available for the preprocessing of the CAMS dataset files.•The emissions and temporal profile registered in CAMS datasets must be compared to official reports of transport sectors.•The development of global datasets such as CAMS is useful for hemispheric analysis and could bring an estimation on the mesoscale.

Air Quality Management for Electroplating Industry for Mumbai Metropolitan Region, Maharashtra—Air Quality Management for Electroplating Industry

Electroplating is considered to be a major polluting industry because it discharges toxic materials and heavy metals through effluent like wastewater, air emissions and solid wastes. There are many registered electroplating units in Mumbai Metropolitan Region (MMR). The quantities of gaseous wastes generated from these industries were estimated and the existing control and treatment techniques for these gaseous wastes were evaluated. Further, Air Quality Modeling (AQM) study was also carried out to predict the concentration of acid mist with the help of emission, characteristics of stack and meteorology. A Gaussian plume model based SCREEN View software was used to predict concentrations for two industries which showed that the acid mist emissions from stack were under the consented limits. Further, health impact survey was performed at 1km radius of the industry to study the effects of air pollution on human health. It showed that 47%, 40% and 57% workers near the electroplating industries are suffering from chest pain, eye irritation and breathlessness respectively. Clustering of electroplating industries in the MMR will improve the waste management in the region. Installation of efficient air pollution control equipment like wet scrubbers can eliminate the hazards caused due to acid mist emissions from electroplating industries.

Aircraft landing and takeoff emission impacts on surface O3 and PM2.5 through aerosol direct feedback effects estimated by the coupled WRF-CMAQ model

The aerosol direct feedback effects (ADFEs) are neglected in traditional air quality modeling studies (where meteorology is used as input and not affected by the chemistry and aerosol microphysics) for estimating the impacts of aircraft emissions on air quality. In this study, aircraft landing and take-off (LTO) attributable change of O3 and PM2.5 concentrations through ADFEs for the year 2005 within the contiguous United States (CONUS) are quantified by a coupled meteorology-chemistry modeling system: Weather Research and Forecasting – Community Multi-scale Air Quality (WRF-CMAQ) model. We first quantified the effects of ADFEs of all aerosols within the CONUS (not the effects of aircraft LTO emissions) on surface meteorology and air quality and found that ADFEs changed on average the downward short-wave radiation (SWR), 2-m temperature (T2), planetary boundary layer (PBL) height, O3 and PM2.5 by −7.38 W/m2, −0.47 K, −20.72 m, −0.41 ppb and +0.28 μg/m3 in 2005. We also found a seasonal influence where ADFE-influenced change (decrease) of SWR, T2, PBL, O3 and change (increase) of PM2.5 were higher in summer than in winter. We found that the aircraft LTO emissions’ contribution to domain average surface concentration of O3 and PM2.5 were +0.0065 ppb and +0.0022 μg/m3 respectively when ADFEs are accounted for. The ADFEs decrease aircraft LTO attributable surface O3 and PM2.5 change by 21% and 23% respectively comparing with that without ADFE in 2005. We also found that in both without-and-with ADFE cases, the aircraft LTO emissions increases domain average of O3 from April to October and decreases from November to March showing a strong seasonal pattern. Our modeling study revealed that use of a coupled model with ADFE shows localized changes in air quality by aircraft LTO emissions across the domain which were masked when looking at domain averages for both O3 and PM2.5, and which may be important for accurately quantifying health risk due to air pollution exposures in densely populated areas.

Emission control for volatile organic compounds from gasoline stations and implication on ozone-forming potential

Fugitive emissions of toxic volatile organic compounds (VOCs) from fuel distribution systems are largely overlooked in air quality management (AQM) in developing Asia. We estimated annual VOCs emissions, associated ozone-forming potential (OFP) and global warming potential (GWP) from gasoline stations in Vietnam for 2010–2016. Current emission control included submerged filling for underground tanks and no vapor recovery for refueling vehicles. Estimated VOCs emissions in 2016 of 20.9 Gg (54.8 Gg of OFP, 292 Gg of 20-yr GWP) were 1.4 times above those in 2010. Spatially, VOCs emissions had high intensity over large urban areas and large economic regions, with Southeast economic region and Red River Delta shared 30% and 25% of the national emissions, respectively. High OFP intensities found over large urban areas would worsen ozone pollution where VOC-sensitive regime presents. Monthly emissions were higher during summer and winter holidays coinciding with intensive transportation. Hourly emissions showed peaks in morning and afternoon rush hours. Implementation of vapor recovery of USEPA Stage I (underground tank filling) and Stage II (vehicle refueling) control, respectively, would reduce VOCs emissions by 38% and 45%, while simultaneous implementations of both reduce emissions by 83%. Equal reductions would be seen for associated OFP/GWP. Control of VOCs emissions from gasoline stations helps improve air quality by reducing ambient levels of toxic VOCs and secondary pollutants of ozone and fine particles, and gain climate co-benefits through GWP reduction. Prepared spatial and temporal VOCs emissions distributions are useful for local AQM and air quality modeling studies.

Improving the estimating accuracy of extinction coefficient of surface aerosol with a new layer-resolved model in China

The aerosol extinction coefficient was an important factor for air quality. To estimate the aerosol extinction levels, widely used pure statistical models are generally not based on aerosol vertical structures. In this study, we estimated large-scale aerosol extinction coefficients by developing a new layer-resolved model with explicit inference for aerosol vertical distribution. The CALIOP aerosol profile, MODIS AOD and reanalysis boundary layer height data are used. The layer-resolved model was formulated by developing an explicit, steady and straightforward relationship between aerosol within boundary layer and corresponding AOD values. The estimated surface extinction coefficient from this model was compared against the values derived from station visibility observations in China in 2016. The results revealed that our model had outperformed the traditional one-layer model and the simplified two-layer model. Specifically, the numbers of ground stations with an NME value < 0.4 are enhanced by a percentage > 100%, with the NME values significantly decreased from 46%, 48% to 36% and RMSE values from 0.27, 0.25 to 0.21 km−1. Our model is easy for operational implementation thanks to its clear structure and input, and also informative to understand aerosol vertical distributions. Furthermore, this work will also be beneficial to air quality modeling studies to improve accuracy estimating ground-level PM2.5 concentrations.

Ethylene Oxide Exposure Attribution and Emissions Quantification Based on Ambient Air Measurements near a Sterilization Facility.

Ethylene oxide (EtO) is a known carcinogen and mutagen associated with increased incidence of breast and blood cancers. The largest medical sterilization facility in Michigan had been assessed by the U.S. Environmental Protection Agency as imposing an additional cancer risk greater than one in one thousand in nearby neighborhoods. This prompted the Michigan Department of Environmental Quality (now referred to as the Department of Environment, Great Lakes, and Energy) to conduct an air quality modeling study of the ambient EtO impacts of the sterilization facility, followed by 24 h Summa canister sampling and TO-15 analysis in two phases. Inverse modeling of the measured 24 h EtO concentrations during the second phase yielded estimates of 594 lbs/year for the facility’s total emissions of EtO and 0.247 µg/m3 for the urban background concentration. The inverse-modeled emissions are similar to reported emissions by the facility operator based on indoor air measurements and simple mass balance assumptions, while the inferred background concentration agrees with estimates from other field investigations. The estimated peak 24 h exposure to EtO caused by the sterilization facility in nearby neighborhoods was 1.83 μg/m3 above the background level, corresponding to an additional cancer risk of approximately one in one hundred, if assumed to represent annual mean exposure.

Local characteristics of and exposure to fine particulate matter (PM2.5) in four indian megacities

Public health in India is gravely threatened by severe PM2.5 exposure. This study presents an analysis of long-term PM2.5 exposure in four Indian megacities (Delhi, Chennai, Hyderabad and Mumbai) based on in-situ observations during 2015–2018, and quantifies the health risks of short-term exposure during Diwali Fest (usually lasting for ~5 days in October or November and celebrating with lots of fireworks) in Delhi for the first time. The population-weighted annual-mean PM2.5 across the four cities was 72 μg/m3, ~3.5 times the global level of 20 μg/m3 and 1.8 times the annual criterion defined in the Indian National Ambient Air Quality Standards (NAAQS). Delhi suffers the worst air quality among the four cities, with citizens exposed to ‘severely polluted’ air for 10% of the time and to unhealthy conditions for 70% of the time. Across the four cities, long-term PM2.5 exposure caused about 28,000 (95% confidence interval: 17,200–39,400) premature mortality and 670,000 (428,900–935,200) years of life lost each year. During Diwali Fest in Delhi, average PM2.5 increased by ~75% and hourly concentrations reached 1676 μg/m3. These high pollutant levels led to an additional 20 (13–25) daily premature mortality in Delhi, an increase of 56% compared to the average over October–November. Distinct seasonal and diurnal variations in PM2.5 were found in all cities. PM2.5 mass concentrations peak during the morning rush hour in all cities. This indicates local traffic could be an important source of PM2.5, the control of which would be essential to improve air quality. We report an interesting seasonal variation in the diurnal pattern of PM2.5 concentrations, which suggests a 1–2 h shift in the morning rush hour from 8 a.m. in pre-monsoon/summer to 9–10 a.m. in winter. The difference between PM2.5 concentrations on weekdays and weekend, namely weekend effect, is negligible in Delhi and Hyderabad, but noticeable in Mumbai and Chennai where ~10% higher PM2.5 concentrations were observed in morning rush hour on weekdays. These local characteristics provide essential information for air quality modelling studies and are critical for tailoring the design of effective mitigation strategies for each city.

Development of air quality forecasting system in Macedonia, based on WRF-Chem model

Urban air quality is composed of a complex interaction of factors associated with anthropogenic emissions, atmospheric circulation, and geographic factors; also, most of the urban pollution presents aerosols and trace gases toxic to human health and responsible for damaged flora, fauna, and materials. The air quality system based on the state-of-the-art Weather Research and Forecasting model coupled with chemistry (WRF-Chem) has been configured and designed for Macedonia. An extensive set of experiments have been performed with different model settings to forecast simultaneously the weather and air pollution over Macedonia. For the present work, a numerical simulation of extreme pollution episode over the urban area in Skopje has been simulated using 1-km WRF-Chem model and a newly developed set of urban mobile emissions. The modeled results implied that the model is very sensitive to the initial meteorological conditions, grid spacing, and mobile emissions. The results are validated against available observations for meteorological fields and pollutant concentrations. While the model forecasts with coarser horizontal grid resolution well fit with the hourly values of some air quality monitoring stations, these experiments tend to underpredict the peak level of about 1250 μg/m3 evidenced at the air quality station “Karpos.” A WRF-Chem 5-km simulation shows increased PM10 concentrations with a peak value of about 1325 μg/m3 a few hours before the evidenced extreme concentration while 2.5-km grid run indicates the initial accumulation of air pollution with relatively high concentration with peak PM10 on 5 February 2017 at 0100 UTC. The 1-km model configuration well captures the PM10 distribution over Skopje valley and the location and timing of the maximum measured air pollution. The verification analysis indicates that the best performance in the sense of the correlation coefficient is achieved with the 5-km WRF-Chem v.3.91 forecast. Numerical simulations with coarser grid resolution show less confidence and lower CC. The results suggest that model initialization and initial data using a mobile source emission provide a better quantitative assessment of extreme air pollution in urban areas. Overall, the present case study shows that the WRF-Chem model has an acceptable performance for meteorological variables as well as PM10 concentration over Skopje. This study provides a general overview of WRF-Chem simulations and can serve as a reference for future air quality modeling studies.