Assessment of air quality using AERMOD modeling: a case study in the Middle East.

Emissions of greenhouse gases from industrial facilities, such as refineries, are one of the most significant environmental problems in many countries. This study aimed to assess the present status of emission sources near a gas refinery region, and the contribution of sources to air pollution was estimated by monitoring CO for a year at a fixed station. This descriptive-analytical study was conducted between January and December 2020. A simulation of CO gas distribution and pollutant concentration prediction was carried out. The results show that the maximum concentration of CO in the 1-h period was 2260 µg/m3, which corresponds to the peak concentration in spring, and in the 8-h period, it was 573 µg/m3, which corresponds to the peak concentration in winter. The studied area's maximum pollutant concentration was also compared to national and international standards for clean air. In all four seasons, the maximum simulated CO concentrations were lower than the Iranian and EPA standards for clean air. Maximum concentrations have occurred in the southern slopes of the study area's heights, and, due to the appropriate wind speed, maximum concentrations in the northeastern mountain peaks occurred at a more considerable distance due to the high altitude of the mountains and the lack of suitable conditions for pollutant escape. Furthermore, because of the height of smokestacks and flares from the ground and the effect of wind on the release height, the concentration of pollutants at the foot of the stacks is low and decreases gradually over a certain distance. Finally, the distribution and deposition of pollutants in the pathway of the smoke were influenced by the type of topography.

Predicting intraurban PM2.5 concentrations using enhanced machine learning approaches and incorporating human activity patterns.

Urban areas contribute substantially to human exposure to ambient air pollution. Numerous statistical prediction models have been used to estimate ambient concentrations of fine particulate matter (PM2.5) and other pollutants in urban environments, with some incorporating machine learning (ML) algorithms to improve predictive power. However, many ML approaches for predicting ambient pollutant concentrations to date have used principal component analysis (PCA) with traditional regression algorithms to explore linear correlations between variables and to reduce the dimensionality of the data. Moreover, while most urban air quality prediction models have traditionally incorporated explanatory variables such as meteorological, land use, transportation/mobility, and/or co-pollutant factors, recent research has shown that local emissions from building infrastructure may also be useful factors to consider in estimating urban pollutant concentrations. Here we propose an enhanced ML approach for predicting urban ambient PM2.5 concentrations that hybridizes cascade and PCA methods to reduce the dimensionality of the data-space and explore nonlinear effects between variables. We test the approach using different durations of time series air quality datasets of hourly PM2.5 concentrations from three air quality monitoring sites in different urban neighborhoods in Chicago, IL to explore the influence of dynamic human-related factors, including mobility (i.e., traffic) and building occupancy patterns, on model performance. We test 9 state-of-the-art ML algorithms to find the most effective algorithm for modeling intraurban PM2.5 variations and we explore the relative importance of all sets of factors on intraurban air quality model performance. Results demonstrate that Gaussian-kernel support vector regression (SVR) was the most effective ML algorithm tested, improving accuracy by 118% compared to a traditional multiple linear regression (MLR) approach. Incorporating the enhanced approach with SVR algorithm increased model performance up to 18.4% for yearlong and 98.7% for month-long hourly datasets, respectively. Incorporating assumptions for human occupancy patterns in dominant building typologies resulted in improvements in model performance by between 4% and 37%. Combined, these innovations can be used to improve the performance and accuracy of urban air quality prediction models compared to conventional approaches.

Modeling the transition behaviors of PM10 pollution index.

Modeling and evaluating the behavior of particulate matter (PM10) is an important step in obtaining valuable information that can serve as a basis for environmental risk management, planning, and controlling the adverse effects of air pollution. This study proposes the use of a Markov chain model as an alternative approach for deriving relevant insights and understanding of PM10 data. Using first- and higher-order Markov chains, we analyzed daily PM10 index data for the city of Klang, Malaysia and found the Markov chain model to fit the PM10 data well. Based on the fitted model, we comprehensively describe the stochastic behaviors in the PM10 index based on the properties of the Markov chain, including its states classification, ergodic properties, long-term behaviors, and mean return times. Overall, this study concludes that the Markov chain model provides a good alternative technique for obtaining valuable information from different perspectives for the analysis of PM10 data.

Dispersion of NO2 and SO2 pollutants in the rolling industry with AERMOD model: a case study to assess human health risk.

Steel and rolling industry are the most important industries polluting the environment. Therefore, aim of this study is to make an emission model for SO2 and NO2 pollutants released from the rolling industry of Sepid-Farab Kavir Steel (SKS) complex using the AERMOD model and health risk assessment. Sampling pollutants released from SKS complex was performed in January 2017 at 10 different sites. Distribution of these pollutants was investigated by AERMOD model, domain site of AERMOD was designed for area around the factory with a radius of 30 km, and also SO2 and NO2 modeling was performed for both natural gas and liquid fuel. Human health risk assessment was also studied. The results of this study demonstrated the emission of SO2 and NO2 from this complex is less than the maximum allowable, when used natural gas as the main fuel. The hourly concentration of SO2 reached about 324 µg/m3, which in higher than the standard value for 1 h. Considering the findings, the urban gas is considered as a clean source in terms of furnace air output and the concentration of emitted pollutants. Also, it has no side effects on workers' health.

Mixed POT-BM Approach for Modeling Unhealthy Air Pollution Events.

This article proposes a novel data selection technique called the mixed peak-over-threshold-block-maxima (POT-BM) approach for modeling unhealthy air pollution events. The POT technique is employed to obtain a group of blocks containing data points satisfying extreme-event criteria that are greater than a particular threshold u. The selected groups are defined as POT blocks. In parallel with that, a declustering technique is used to overcome the problem of dependency behaviors that occurs among adjacent POT blocks. Finally, the BM concept is integrated to determine the maximum data points for each POT block. Results show that the extreme data points determined by the mixed POT-BM approach satisfy the independent properties of extreme events, with satisfactory fitted model precision results. Overall, this study concludes that the mixed POT-BM approach provides a balanced tradeoff between bias and variance in the statistical modeling of extreme-value events. A case study was conducted by modeling an extreme event based on unhealthy air pollution events with a threshold u > 100 in Klang, Malaysia.

Benzene emissions from gas station clusters: a new framework for estimating lifetime cancer risk.

PURPOSE: During gas station operation, unburned fuel can be released to the environment through distribution, delivery, and storage. Due to the toxicity of fuel compounds, setback distances have been implemented to protect the general population. However, these distances treat gasoline sales volume as a categorical variable and only account for the presence of a single gas station and not clusters, which frequently occur. This paper introduces a framework for recommending setback distances for gas station clusters based on estimated lifetime cancer risk from benzene exposure. METHODS: Using the air quality dispersion model AERMOD, we simulated levels of benzene released to the atmosphere from single and clusters of generic gas stations and the associated lifetime cancer risk under meteorological conditions representative of Albany, New York. RESULTS: Cancer risk as a function of distance from gas station(s) and as a continuous function of total sales volume can be estimated from an equation we developed. We found that clusters of gas stations have increased cancer risk compared to a single station because of cumulative emissions from the individual gas stations. For instance, the cancer risk at 40 m for four gas stations each dispensing 1 million gal/year is 9.84 × 10-6 compared to 2.45 × 10-6 for one gas station. CONCLUSION: The framework we developed for estimating cancer risk from gas station(s) could be adopted by regulatory agencies to make setback distances a function of sales volume and the number of gas stations in a cluster, rather than on a sales volume category. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40201-020-00601-w.

Comparisons of simple and complex methods for quantifying exposure to individual point source air pollution emissions.

Expanded use of reduced complexity approaches in epidemiology and environmental justice investigations motivates detailed evaluation of these modeling approaches. Chemical transport models (CTMs) remain the most complete representation of atmospheric processes but are limited in applications that require large numbers of runs, such as those that evaluate individual impacts from large numbers of sources. This limitation motivates comparisons between modern CTM-derived techniques and intentionally simpler alternatives. We model population-weighted PM2.5 source impacts from each of greater than 1100 coal power plants operating in the United States in 2006 and 2011 using three approaches: (1) adjoint PM2.5 sensitivities calculated by the GEOS-Chem CTM; (2) a wind field-based Lagrangian model called HyADS; and (3) a simple calculation based on emissions and inverse source-receptor distance. Annual individual power plants' nationwide population-weighted PM2.5 source impacts calculated by HyADS and the inverse distance approach have normalized mean errors between 20 and 28% and root mean square error ranges between 0.0003 and 0.0005 µg m-3 compared with adjoint sensitivities. Reduced complexity approaches are most similar to the GEOS-Chem adjoint sensitivities nearby and downwind of sources, with degrading performance farther from and upwind of sources particularly when wind fields are not accounted for.

Vent pipe emissions from storage tanks at gas stations: Implications for setback distances.

At gas stations, fuel vapors are released into the atmosphere from storage tanks through vent pipes. Little is known about when releases occur, their magnitude, and their potential health consequences. Our goals were to quantify vent pipe releases and examine exceedance of short-term exposure limits to benzene around gas stations. At two US gas stations, we measured volumetric vent pipe flow rates and pressure in the storage tank headspace at high temporal resolution for approximately three weeks. Based on the measured vent emission and meteorological data, we performed air dispersion modeling to obtain hourly atmospheric benzene levels. For the two gas stations, average vent emission factors were 0.17 and 0.21 kg of gasoline per 1000 L dispensed. Modeling suggests that at one gas station, a 1-hour Reference Exposure Level (REL) for benzene for the general population (8 ppb) was exceeded only closer than 50 m from the station's center. At the other gas station, the REL was exceeded on two different days and up to 160 m from the center, likely due to non-compliant bulk fuel deliveries. A minimum risk level for intermediate duration (>14-364 days) benzene exposure (6 ppb) was exceeded at the elevation of the vent pipe opening up to 7 and 8 m from the two gas stations. Recorded vent emission factors were >10 times higher than estimates used to derive setback distances for gas stations. Setback distances should be revisited to address temporal variability and pollution controls in vent emissions.

A hybrid modeling framework to estimate pollutant concentrations and exposures in near road environments.

Traffic related air pollution is one of the major local sources of pollution challenging most urban populations. Current air quality modeling approaches can estimate the concentrations of air pollutants on either regional or local scales but cannot effectively estimate concentrations from the combination of regional and local sources at both local and regional scales simultaneously. This study describes a hybrid modeling framework, HYCAMR, combining a regional model, CAMx, and a local-scale dispersion model, R-LINE, to estimate concentrations of both primary and secondary species at high temporal (hourly) and spatial (40 m) resolution. HYCAMR utilizes all the chemical and physical processes available in CAMx and the Particulate Matter Source Apportionment Technology (PSAT) tool to estimate concentrations from both onroad and nonroad emission sources. HYCAMR employs R-LINE, to estimate the normalized dispersion of pollutant mass from onroad emission sources, from primary and secondary roads, at high resolution. Applying R-LINE for one day per month using average daily meteorology yields seasonally-resolved spatial dispersion profiles at low computational cost. Combining the R-LINE spatial dispersion profile with CAMx concentration estimates yields an estimate of the combined concentrations for a range of pollutants at high spatial and temporal resolution. In three major cities in Connecticut, HYCAMR shows strong temporal and seasonal variability in NOx, PM2.5, and elemental carbon (EC) concentrations. This study evaluates HYCAMR year 2011 estimates of NO2 and PM2.5 against two sources: satellite-based estimates at coarse resolution and regression model estimates at census block group resolution. In this evaluation, HYCAMR demonstrates improved agreement with the land-use regression modeling and mixed agreement with satellite-based estimates when compared to the regional CAMx estimates.

Parameterization and evaluation of the CALMET/CALPUFF model system in near-field and complex terrain - Terrain data, grid resolution and terrain adjustment method.

Effective and accurate modeling of air quality in complex terrain constitutes one of the main challenges for the community of modelers. One of the basic problems is the selection of input data of adequate quality in combination with a uniform configuration of the modeling systems. Simultaneously, the primary aim is to obtain predictions of satisfying accuracy. In the article the results of the evaluation for the CALMET/CALPUFF modeling system in near-field and complex terrain were presented. Research was conducted based on three experimental databases for the dispersion model evaluation, i.e. Martin's Creek (SO2), Lovett (SO2) and Tracy Power Plant (SF6). Each experiment concerned an area characterized by different topography, meteorological conditions, emission source features and the location of tracer substance monitors (SO2, SF6). The aim of the study was to determine the optimal settings for the CALMET/CALPUFF models with regard to the digital elevation model dataset (GTOPO30, SRTM3, NED), grid resolution (ranging from 100 to 4000 m) and the terrain adjustment methods available in the CALPUFF model (MCTADJ = 0, 1, 2 and 3). The results of the CALMET/CALPUFF accuracy evaluation showed, that the use of the digital elevation model (DEM) with a horizontal accuracy of approximately 90 m and a vertical accuracy of 15 m (SRTM3) is sufficient. Application of more accurate data (NED) resulted in comparable model evaluation outcomes. Using both dense and coarse grids resulted in FB and/or FBRHC higher than 0.6. Omitting the terrain adjustment method in the CALPUFF model results in the twofold underestimation of the measurements. The best results of the CALMET/CALPUFF accuracy evaluation for each experiment (|FB| < 0.30, FAC = 1.0, NAD ≤ 0.15, VG < 1.12, IOA > 0.5) were achieved for the grid resolution of 400 m with the use of a partial plume path adjustment method (MCTADJ = 3).

Exposure estimation errors to nitrogen oxides on a population scale due to daytime activity away from home.

Accurate estimation of exposure to air pollution is necessary for assessing the impact of air pollution on the public health. Most environmental epidemiology studies assign the home address exposure to the study subjects. Here, we quantify the exposure estimation error at the population scale due to assigning it solely at the residence place. A cohort of most schoolchildren in Israel (~950,000), age 6-18, and a representative cohort of Israeli adults (~380,000), age 24-65, were used. For each subject the home and the work or school addresses were geocoded. Together, these two microenvironments account for the locations at which people are present during most of the weekdays. For each subject, we estimated ambient nitrogen oxide concentrations at the home and work or school addresses using two air quality models: a stationary land use regression model and a dynamic dispersion-like model. On average, accounting for the subjects' work or school address as well as for the daily pollutant variation reduced the estimation error of exposure to ambient NOx/NO2 by 5-10ppb, since daytime concentrations at work/school and at home can differ significantly. These results were consistent regardless which air quality model as used and even for subjects that work or study close to their home. Yet, due to their usually short commute, assigning schoolchildren exposure solely at their residential place seems to be a reasonable estimation. In contrast, since adults commute for longer distances, assigning exposure of adults only at the residential place has a lower correlation with the daily weighted exposure, resulting in larger exposure estimation errors. We show that exposure misclassification can result from not accounting for the subjects' time-location trajectories through the spatiotemporally varying pollutant concentrations field.

Personal sampler for monitoring of viable viruses; modelling of outdoor sampling conditions.

A new personal bioaerosol sampler has recently been developed and verified to be very efficient for monitoring of viable airborne bacteria, fungi and viruses. The device is capable of providing high recovery rates even for microorganisms which are rather sensitive to physical and biological stresses. However, some mathematical procedure is required for realistic calculation of an actual concentration of viable bioaerosols in the air taking into account a rate of inactivation of targeted microorganisms, sampling parameters, and results of microbial analysis of collecting liquid from the sampler. In this paper, we develop such procedure along with the model of aerosol propagation for outdoor conditions. Combining these procedures allows one to determine the optimal sampling locations for the best possible coverage of the area to be monitored. A hypothetical episode concerned with terrorists' attack during music concert in the central square of Novosibirsk, Russia was considered to evaluate possible coverage of the area by sampling equipment to detect bioaerosols at various locations within the square. It was found that, for chosen bioaerosol generation parameters and weather conditions, the new personal sampler would be capable to reliably detect pathogens at all locations occupied by crowd, even at distances of up to 600 m from the source.