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Land use regression (LUR) modeling has become a common method for predicting pollutant concentrations and assigning exposure estimates in epidemiological studies. However, few LUR models have been developed for metal constituents of fine particulate matter (PM2.5) or have incorporated source-specific dispersion covariates in locations with major point sources. We developed hybrid AERMOD LUR models for PM2.5, black carbon (BC), and steel-related PM2.5 constituents lead, manganese, iron, and zinc, using fine-scale air pollution data from 37 sites across the Pittsburgh area. These models were designed with the aim of developing exposure estimates for time periods of interest in epidemiology studies. We found that the hybrid LUR models explained greater variability in PM2.5 (R2 = 0.79) compared to BC (R2 = 0.59) and metal constituents (R2 = 0.34–0.55). Approximately 70% of variation in PM2.5 was attributable to temporal variance, compared to 36% for BC, and 17–26% for metals. An AERMOD dispersion covariate developed using PM2.5 industrial emissions data for 207 sources was significant in PM2.5 and BC models; all metals models contained a steel mill-specific PM2.5 emissions AERMOD term. Other significant covariates included industrial land use, commercial and industrial land use, percent impervious surface, and summed railroad length.

The effectiveness and cost are always top factors for policy-makers to decide control measures and most measures had no pre-test before implementation. Due to the COVID-19 pandemic, human activities are largely restricted in many regions in India since mid-March of 2020, and it is a progressing experiment to testify effectiveness of restricted emissions. In this study, concentrations of six criteria pollutants, PM10, PM2.5, CO, NO2, ozone and SO2 during March 16th to April 14th from 2017 to 2020 in 22 cities covering different regions of India were analysed. Overall, around 43, 31, 10, and 18% decreases in PM2.5, PM10, CO, and NO2 in India were observed during lockdown period compared to previous years. While, there were 17% increase in O3 and negligible changes in SO2. The air quality index (AQI) reduced by 44, 33, 29, 15 and 32% in north, south, east, central and western India, respectively. Correlation between cities especially in northern and eastern regions improved in 2020 compared to previous years, indicating more significant regional transport than previous years. The mean excessive risks of PM reduced by ~52% nationwide due to restricted activities in lockdown period. To eliminate the effects of possible favourable meteorology, the WRF-AERMOD model system was also applied in Delhi-NCR with actual meteorology during the lockdown period and an un-favourable event in early November of 2019 and results show that predicted PM2.5 could increase by only 33% in unfavourable meteorology. This study gives confidence to the regulatory bodies that even during unfavourable meteorology, a significant improvement in air quality could be expected if strict execution of air quality control plans is implemented., Graphical abstract Unlabelled Image, Highlights • The effect of restricted human activities due to the COVID-19 pandemic in India on air quality in 22 cities was estimated. • PM2.5 had maximum reduction in most regions. • Correlation between cities especially in northern and eastern regions improved in 2020 compared to previous years. • The substantial reduction in concentrations resulted in a 4 times reduction in total ER. • PM2.5 could increase due to unfavourable meteorology but the average concentration would still be under CPCB limits.

Gaussian dispersion models are widely used to simulate the concentrations and deposition fluxes of pollutants emitted by source areas. Very often, the calculation time limits the number of sources and receptors and the geometry of the sources must be simple and without holes. This paper presents CAREA, a new GIS-based Gaussian model for complex source areas. CAREA was coded in the Python language, and is largely based on a simplified formulation of the very popular and recognized AERMOD model. The model allows users to define in a GIS environment thousands of gridded or scattered receptors and thousands of complex sources with hundreds of vertices and holes. CAREA computes ground level, or near ground level, concentrations and dry deposition fluxes of pollutants. The input/output and the runs of the model can be completely managed in GIS environment (e.g. inside a GIS project). The paper presents the CAREA formulation and its applications to very complex test cases. The tests shows that the processing time are satisfactory and that the definition of sources and receptors and the output retrieval are quite easy in a GIS environment. CAREA and AERMOD are compared using simple and reproducible test cases. The comparison shows that CAREA satisfactorily reproduces AERMOD simulations and is considerably faster than AERMOD.

Anoxic/anaerobic/oxic (A2/O) wastewater treatment has emerged as a major process for treatment of domestic wastewater. One of the issues with wastewater treatment plants (WWTPs) is that volatile sulfur compounds (VSCs) are discharged from them and pose numerous health risks. This study characterized VSC emissions at the water-air interface and concentrations of ambient air exposure from different treatment units in an A2/O WWTP. AERMOD modeling was used to simulate the atmospheric behaviors of discharged VSCs. Results demonstrated that VSC emission fluxes and exposure concentrations had followed a descending order of pretreatment>biological treatment>advanced treatment. Emissions were affected by sulfate concentrations and chemical oxygen demand in the wastewater, and control strategies based on these values were proposed. The AERMOD results indicated that the majority of the total hydrogen sulfide (87%) and methyl mercaptan (65%) emissions came from the primary sedimentation tank, while the majority of dimethyl sulfide (81%), carbon disulfide (84%), and dimethyl disulfide (93%) were emitted from the oxic area. MT and DMS were the main odorous components of the VSCs in ambient air based on the indicator of odor activity values. Noncancer health risks, determined by having a hazard quotient >1, of the measured VSCs were beyond acceptable limits. Overall, efforts should be made to minimize noncancer health risks as individuals are exposed to VSCs not only in treatment units but also in areas surrounding WWTPs.

To date, phosphate surface mining suffers from lack of reliable emission factors. Due to complete absence of data to derive emissions factors, we developed a methodology for estimating them indirectly by studying a range of possible emission factors for surface phosphate mining operations and comparing AERMOD calculated concentrations to concentrations measured around the mine. We applied this approach for the Khneifiss phosphate mine, Syria, and the Al-Hassa and Al-Abyad phosphate mines, Jordan. The work accounts for numerous model unknowns and parameter uncertainties by applying prudent assumptions concerning the parameter values. Our results suggest that the net mining operations (bulldozing, grading and dragline) contribute rather little to ambient TSP concentrations in comparison to phosphate processing and transport. Based on our results, the common practice of deriving the emission rates for phosphate mining operations from the US EPA emission factors for surface coal mining or from the default emission factor of the EEA seems to be reasonable. Yet, since multiple factors affect dispersion from surface phosphate mines, a range of emission factors, rather than only a single value, was found to satisfy the model performance.

This study evaluates AERMOD and CALPUFF dispersion calculations of particulate matter emissions from stone quarries in two mountainous regions against TSP and PM10 measurements, using both observational and WRF-modeled meteorological data. Due to different model parameterization, AERMOD dispersion predictions were in better agreement with the measured concentrations than those obtained by CALPUFF. As expected, the smaller the distance between the meteorological station, the source (quarry) and the receptors, the better the predictions of both AERMOD and CALPUFF. In contrast, using in-situ wind field obtained by runs of the WRF meteorological model for the complex terrain study area provided, in general, less accurate dispersion estimates than when using (even remote) meteorological observations. In particular, using the three-dimensional WRF-modeled wind field within CALPUFF did not provide any advantage over using the two-dimensional wind field, which is the common procedure of AERMOD and CALPUFF. Dry deposition was more significant for ambient concentration estimation in AERMOD than in CALPUFF.

In this study, 3990 movements of 629 different ships approaching to the Ambarli port of Istanbul and 10,272 movements of 2798 different ships arriving at Kocaeli port which are the largest ports of Turkey were monitored for a year between September 1, 2017 and September 1, 2018. It is well known that ship exhaust emissions have a significant impact on pollutant and global warming agent mass inputs and diminish air quality around port areas. We calculated ship exhaust emissions for three different modes by ENTEC method. The annual estimated emissions were 72,802, 1430.4, 900.3, 105.3, 60.9, and 59 tons for CO2, NOx, SO2, PM10, VOC, and CO, respectively, in Ambarli Port. Higher emissions were estimated for Kocaeli Port due to marine traffic intensity. The annual estimated emissions at Kocaeli Port were 134,120.8, 2655.1, 1652.3, 181.4, 108.3, and 106.4 tons for CO2, NOx, SO2, PM10, VOC, and CO, respectively. In order to determine the impact of ship emissions, we employed AERMOD air quality dispersion modelling. Ground-level PM10, NOx, and SO2 concentrations were calculated. Their spatial distribution was plotted and results were evaluated by air quality measurement station results. Only estimated SO2 concentrations were higher than the observed concentrations. We concluded that ENTEC calculations produce excessive SO2 concentrations. Because sulphur content of marine fuel may vary and recent amendments in the related regulations limit its amount to lower values. Advancement of on-board SO2 emission control on ships can also have a reducing effect on emitted SO2 emissions. As a result, we emphasize that current ENTEC emission factors don't represent SO2 emissions accurately. Further, the advancement of SO2 emissions factors is required.

Campaigns from 2008 to 2016 are carried out to study temporal variations and environmental impacts of polychlorinated dibenzo-p-dioxin and dibenzofuran (PCDD/Fs) in soils in the vicinity of a new hazardous waste incinerator (HWI) in China. Results indicate that after 8-year operation of HWI, the geometrical means of both the total concentrations and the TEQ values of PCDD/Fs in soils decrease from 1280 ng·kg−1 and 3.08 ng WHO-TEQ·kg−1 to 568 ng·kg−1 and 2.70 ng WHO-TEQ·kg−1, respectively, showing generally limited impact on soils within 7.5 km. Temporal changes of PCDD/Fs congener profiles trend to profiles in combustion sources. Considering the whole studied area, results of principal component analysis between soils and emission sources show that instead of HWI, other sources including open burning, traffic, and cement plant are more responsible for PCDD/Fs accumulation. The modeling results of AERMOD indicate the dominant roles of wind velocities and directions on the deposition of PCDD/Fs emitted from HWI. The largest PCDD/Fs increase value in soils predicted by integrating AERMOD and a reservoir model is very limited after 25 years (2.03 × 10−5 ng WHO-TEQ·kg−1), indicating relatively minor impacts of HWI on surrounding soils, but the noticeable impact on area downwind from the stack in short distance (e.g., within 0.5 km) should be recognized.

Aircraft activity and airport operations can increase combustion-related air pollutant concentrations, but it is difficult to distinguish aviation emissions from traffic and other local sources. Emission inventories are uncertain and dispersion models may not capture aircraft plume complexity; ambient monitoring data require detailed statistical analyses to extract aviation signals. The goal of this study is to compare two modeling approaches including monitoring-based regression models and the EDMS/AERMOD dispersion model, informing improvements and allowing quantitation of aviation impacts on air quality through multi-pollutant sensitivity and multi-monitor fate/transport analyses. Aggregate concentration comparisons are similar, though diurnal patterns show potential weaknesses in near-field dispersion, treatment of overnight conditions, and emission inventory accuracy.

The Lake Wabamun area, in Alberta, is unique within Canada as there are four coal-fired power plants within a 500 km(2) area. Continuous monitoring of ambient total gaseous mercury (TGM) concentrations in the Lake Wabamun area was undertaken at two sites, Genesee and Meadows. The data were analyzed in order to characterise the effect of the coal-fired power plants on the regional TGM. Mean concentrations of 1.57 ng/m(3) for Genesee and 1.50 ng/m(3) for Meadows were comparable to other Canadian sites. Maximum concentrations of 9.50 ng/m(3) and 4.43 ng/m(3) were comparable to maxima recorded at Canadian sites influenced by anthropogenic sources. The Genesee site was directly affected by the coal-fired power plants with the occurrence of northwest winds, and this was evident by episodes of elevated TGM, NO(x) and SO(2) concentrations. NO(x)/TGM and SO(2)/TGM ratios of 21.71 and 19.98 microg/ng, respectively, were characteristic of the episodic events from the northwest wind direction. AERMOD modeling predicted that coal-fired power plant TGM emissions under normal operating conditions can influence hourly ground-level concentrations by 0.46-1.19 ng/m(3)(.) The effect of changes in coal-fired power plant electricity production on the ambient TGM concentrations was also investigated, and was useful in describing some of the episodes.