Modeling of particulate matter dispersion from a poultry facility using AERMOD

CALINE4 and AERMOD modelling for roadway vehicle-related air pollution: a recent review in India

Modelling and prediction of air quality facilitates the drafting of efficient guidelines and, in turn, proper management of adversely affected areas. In order to depict the air pollutants in urban centres, this research analyses two modelling tools: AERMOD and CALINE4. Both technologies provide distinct capabilities in the modelling of air quality from vehicular and other emissions. CALINE4, a Gaussian dispersion model, specifies pollutant dispersion from mobile sources along roadways. It boasts a user-friendly interface and road-specific modelling capabilities, factoring in traffic speed and vehicle emissions. However, it simplifies intricate flow patterns and relies on primary meteorological data. On the other hand, AERMOD is a versatile model suitable for various emission sources, including both mobile and stationary sources. It excels at capturing diverse atmospheric processes but demands precise meteorological, terrain, and emission data. AERMOD is often preferred for regulatory compliance assessments, although it entails a steeper learning curve and higher computational requirements. The choice between CALINE4 and AERMOD hinges on study needs, data availability, and desired modelling precision. This review offers an overview to assist researchers in making informed model selections for assessing vehicle-related pollution, critical aspects of urban sustainability, and air quality management.

Indoor air quality improvement with filtration and UV-C on mitigation of particulate matter and airborne bacteria: Monitoring and modeling

Indoor air, especially with suspended particulate matter (PM), can be a carrier of airborne infectious pathogens. Without sufficient ventilation, airborne infectious diseases can be transmitted from one person to another. Indoor air quality (IAQ) significantly impacts people's daily lives as people spend 90% of their time indoors. An industrial-grade air cleaner prototype (filtration + ultraviolet light) was previously upgraded to clean indoor air to improve IAQ on two metrics: particulate matter (PM) and viable airborne bacteria. Previous experiments were conducted to test its removal efficiency on PM and airborne bacteria between the inlet and treated air. However, the longer-term improvement on IAQ would be more informative. Therefore, this research focused on quantifying longer-term improvement in a testing environment (poultry facility) loaded with high and variable PM and airborne bacteria concentrations. A 25-day experiment was conducted to treat indoor air using an air cleaner prototype with intermittent ON and OFF days in which PM and viable airborne bacteria were measured to quantify the treatment effect. The results showed an average of 55% reduction of total suspended particulate (TSP) concentration between OFF days (110 μg/m3) and ON days (49 μg/m3). An average of 47% reduction of total airborne viable bacteria concentrations was achieved between OFF days (∼3200 CFU/m3) and ON days (∼2000 CFU/m3). A cross-validation (CV) model was established to predict PM concentrations with five input variables, including the status of the air cleaner, time (h), ambient temperature, indoor relative humidity, and day of the week to help simulate the air-cleaning effect of this prototype. The model can approximately predict the air quality trend, and future improvements may be made to improve its accuracy.

Improving modeling of low-altitude particulate matter emission and dispersion: A cotton gin case study

Monitoring and modeling of airborne particulate matter (PM) from low-altitude sources is becoming an important regulatory target as the adverse health consequences of PM become better understood. However, application of models not specifically designed for simulation of PM from low-altitude emissions may bias predictions. To address this problem, we describe the modification and validation of an air dispersion model for the simulation of low-altitude PM dispersion from a typical cotton ginning facility. We found that the regulatory recommended model (AERMOD) overestimated pollutant concentrations by factors of 64.7, 6.97 and 7.44 on average for PM_2.5, PM₁₀, and TSP, respectively. Pollutant concentrations were negatively correlated with height (p < 0.05), distance from source (p < 0.05) and standard deviation of wind direction (p < 0.001), and positively correlated with average wind speed (p < 0.001). Based on these results, we developed dispersion correction factors for AERMOD and cross-validated the revised model against independent observations, reducing overestimation factors to 3.75, 1.52 and 1.44 for PM_2.5, PM₁₀ and TSP, respectively. Further reductions in model error may be obtained from use of additional observations and refinement of dispersive correction factors. More generally, the correction permits the validated adjustment and application of pre-existing models for risk assessment and development of remediation techniques. The same approach may also be applied to improve simulations of other air pollutants and environmental conditions of concern.

Effectiveness and diurnal variations of vegetative environmental buffers (VEBs) for mitigating NH3 and PM emissions from poultry houses

Air pollutants from poultry production, such as ammonia (NH3) and particulate matter (PM), have raised concerns due to their potential negative impacts on human health and the environment. Vegetative environmental buffers (VEBs), consisting of trees and/or grasses planted around poultry houses, have been investigated as a mitigation strategy for these emissions. Although previous research demonstrated that VEBs can reduce NH3 and PM emissions, these studies used a limited number of samplers and did not examine concentration profiles. Moreover, the differences between daytime and nighttime emissions have not been investigated. In this study, we characterized emission profiles from a commercial poultry house using an array with multiple sampling heights and explored the differences between daytime and nighttime NH3 and PM profiles. We conducted three sampling campaigns, each with ten sampling events (five daytime and five nighttime), at a VEB-equipped poultry production facility. NH3 and PM samples were collected downwind from the ventilation tunnel fans before, within, and after the VEB. Results showed that ground-level concentrations beyond the VEB decreased to 8.0% ± 2.7% for NH3, 13% ± 4% for TSP, 13% ± 4% for PM10, and 2.4% ± 2.8% for PM2.5 of the original concentrations from the exhaust tunnel fan, with greater reduction efficiency during daytime than nighttime. Furthermore, pollutant concentrations were positively intercorrelated. These findings will be valuable for developing more effective pollutant remediation strategies in poultry house emissions.

Meteorological data source comparison-a case study in geospatial modeling of potential environmental exposure to abandoned uranium mine sites in the Navajo Nation

Meteorological (MET) data is a crucial input for environmental exposure models. While modeling exposure potential using geospatial technology is a common practice, existing studies infrequently evaluate the impact of input MET data on the level of uncertainty on output results. The objective of this study is to determine the effect of various MET data sources on the potential exposure susceptibility predictions. Three sources of wind data are compared: The North American Regional Reanalysis (NARR) database, meteorological aerodrome reports (METARs) from regional airports, and data from local MET weather stations. These data sources are used as inputs into a machine learning (ML) driven GIS Multi-Criteria Decision Analysis (GIS-MCDA) geospatial model to predict potential exposure to abandoned uranium mine sites in the Navajo Nation. Results indicate significant variations in results derived from different wind data sources. After validating the results from each source using the National Uranium Resource Evaluation (NURE) database in a geographically weighted regression (GWR), METARs data combined with the local MET weather station data showed the highest accuracy, with an average R2 of 0.74. We conclude that local direct measurement-based data (METARs and MET data) produce a more accurate prediction than the other sources evaluated in the study. This study has the potential to inform future data collection methods, leading to more accurate predictions and better-informed policy decisions surrounding environmental exposure susceptibility and risk assessment.

Air Quality Dispersion Modelling to Evaluate CIPP Installation Styrene Emissions

Cured-in-place pipe (CIPP) is one of the most popular in situ rehabilitation techniques to repair sewer and water pipes. While there are multiple approaches to curing CIPP, steam-curing of styrene-based resins has been found to be associated with air-borne chemical emissions. Health officials, utilities and industry representatives have recognized the need to know more about these emissions, especially styrene. Such concern has led to multiple studies investigating the concentrations of volatile organic compounds on CIPP installation sites. This study expands upon previous effort by modeling worst-case, steam-cured CIPP emissions over a 5-year weather record. The effort also includes calibration of the model to emissions averages over the work day rather than instantaneous field measurements. Dispersion modelling software, AERMOD, was utilized to model the styrene component of CIPP emissions on two CIPP installation sites in the US. Based on the analysis results, it was found that the styrene emitted from stacks dissipates rapidly with styrene concentrations only exceeding minimum health and safety threshold levels at distances close to the stack (2 m or less). The values predicted by the model analysis are comparable with the field measured styrene concentrations from other studies. Current safety guidelines in the US recommend a 4.6-m (15-ft) safety perimeter for stack emission points. The results of this study indicate that significant and lasting health impacts are unlikely outside recommended safety perimeter. The results also validate the importance of enforcing recommended safety guidance on steam-cured CIPP sites.

Environmental Risk Assessment for PM2.5 Pollution from Non-Point Sources in the Mining Area Based on Multi-Source Superposition and Diffusion

To identify high-concentration contributing sources and highly dispersive pollution sources of fine particulate matter, analyze the relationship between the location and distribution shape of emission sources and the concentration contribution and dispersion of particulate matter, and realize the atmospheric environment risk simulation and the differential control of non-point sources in the mining area, taking a large mining area in Inner Mongolia as an example, we classified the emission sources of PM2.5 (particulate matter less than 2.5 μm) and complied with the emission inventory. A CALPUFF model was used to simulate the contribution for the PM2.5 concentration of six types of emission sources and a multi-source superposition. Through scenario simulation, we analyzed the relationship between the spatial distribution of emission sources and the emission concentration and diffusion in a large mining area. We analyzed the relative risks of six types of sources under the influence of other superimposed sources and the change of emission concentration during transmission. We established a three-dimensional evaluation model to assess the atmospheric environmental risk of PM2.5 non-point sources in the mining area, considering the change rate of PM2.5 concentration with migration, the relative contribution ratio of superimposed sources, and the equal contribution index of the standard concentration. The results show that the maximum equal contribution index of standard concentration of multi-source superposition was 4.40. Among them, non-paved roads, exposed surface sources of coal piles, and exposed surface sources of mine pits and dumps were the top three pollution contributors, and their maximum equal contribution indexes of standard concentration were 2.40, 2.21, and 2.10, respectively. The effect of superimposed pollution sources was affected by the wind field and the spatial distribution density of emission sources, while the dispersion was affected by the wind direction and the distribution direction of pollution sources. In the case of the same source intensity and emission area, the denser the source distribution was, the higher the emission concentration was, the smaller the contribution ratio of superimposed sources was, and the greater the relative pollution risk was. When the angle between the direction of dispersed linear pollution sources and the dominant wind direction was smaller, the emission concentration was higher, but the diffusion surface was smaller. When the angle with the direction of wind direction was larger, the emission concentration was lower, but the diffusion surface was larger. Concentrated pollution sources had the highest concentration and the diffusion surface was in the middle. Non-paved roads had the highest environmental risk, with an average risk of 5.61 × 10−2, followed by coal piles with an average value of 2.06 × 10−2, followed by pits and dumps with an average value of 1.89 × 10−2; the environmental risk of loading and unloading sources was the lowest. Unpaved roads were pollution sources with high relative pollution risk and diffusion risk, and their average relative pollution risk and diffusion risk were 2.34 × 10−2 and 3.28 × 10−2, respectively. In the case of multi-source superposition, the high-risk areas were mainly heavily polluted areas with intensive emission sources, while the medium-risk areas were moderately polluted areas with scattered pollution sources, and the diffusion risk was high. This research concludes that the dispersed distribution of pollution sources can reduce pollution risk, and the smaller the angle is between the linear distribution direction of pollution sources and the dominant wind direction, the smaller the diffusion risk is. Therefore, differentiated control can be carried out according to the characteristics of pollution sources. The conclusions can provide methods and theoretical support for the control of atmospheric environment risk, pollution prevention, and control planning in mining areas.

Considering Condensable Particulate Matter Emissions Improves the Accuracy of Air Quality Modeling for Environmental Impact Assessment

This study examines environmental impact assessment considering filterable particulate matter (FPM) and condensable particulate matter (CPM) to improve the accuracy of the air quality model. Air pollutants and meteorological data were acquired from Korea’s national monitoring station near a residential development area in the target district and background site. Seasonal emissions of PM2.5, including CPM, were estimated using the California puff (CALPUFF) model, based on Korea’s national emissions inventory. These results were compared with the traditional environmental impact assessment results. For the residential development area, the seasonal PM2.5 concentration was predicted by considering FPM and CPM emissions in the target area as well as the surrounding areas. In winter and spring, air quality standards were not breached because only FPM was considered. However, when CPM was included in the analysis, the results exceeded the air quality standards. Furthermore, it was predicted that air quality standards would not be breached in summer and autumn, even when CPM is included. In other words, conducting an environmental impact assessment on air pollution including CPM affects the final environmental decision. Therefore, it is concluded that PM2.5 should include CPM for greater accuracy of the CALPUFF model for environmental impact assessment.

STHAM: an agent based model for simulating human exposure across high resolution spatiotemporal domains

Human exposure to particulate matter and other environmental species is difficult to estimate in large populations. Individuals can encounter significant and acute variations in exposure over small spatiotemporal scales. Exposure is strongly tied to both the environmental and activity contexts that individuals experience. Here we present the development of an agent-based model to simulate human exposure at high spatiotemporal resolutions. The model is based on simulated activity and location trajectories on a per-person basis for large geographical areas. We demonstrate that the model can successfully estimate trajectories and that activity patterns have been validated against traffic patterns and that can be integrated with exposure-agent geographical distributions to estimate total human exposure.

Residential proximity to concentrated animal feeding operations and allergic and respiratory disease

BACKGROUND

Air emissions from concentrated animal feeding operations (CAFO) have been associated with respiratory and allergic symptoms among farm workers, primarily on swine farms. Despite the increasing prevalence of CAFOs, few studies have assessed respiratory health implications among residents living near CAFOs and few have looked at the health impacts of dairy CAFOs.

OBJECTIVES

The goal of this study was to examine objective and subjective measures of respiratory and allergic health among rural residents living near dairy CAFOs in a general population living in the Upper Midwest of the United States.

METHODS

Data were from the 2008-2016 Survey of the Health of Wisconsin (SHOW) cohort (n = 5338), a representative, population based sample of rural adults (age 18+). The association between distance to the nearest CAFO and the prevalence of self-reported physician-diagnosed allergies, asthma, episodes of asthma in the last 12 months, and asthma medication use was examined using logistic regression, adjusting for covariates and sampling design. Similarly, the association between distance to the nearest CAFO and lung function, measured using spirometry, was examined using multivariate linear regression. Restricted cubic splines accounted for nonlinear relationships between distance to the nearest CAFO and the aforementioned outcomes.

RESULTS

Living 1.5 miles from a CAFO was associated with increased odds of self-reported nasal allergies (OR = 2.08; 95% CI: 1.38, 3.14), lung allergies (OR = 2.72; 95% CI: 1.59, 4.66), asthma (OR = 2.67; 95% CI: 1.39, 5.13), asthma medication (OR = 3.31; 95% CI: 1.65 6.62), and uncontrolled asthma, reported as an asthma episode in last 12 months (OR = 2.34; 95% CI: 1.11, 4.92) when compared to living 5 miles from a CAFO. Predicted FEV1 was 7.72% (95% CI: -14.63, -0.81) lower at a residential distance 1.5 miles from a CAFO when compared with a residence distance of 3 miles from a CAFO.

CONCLUSIONS

Results suggest CAFOs may be an important source of adverse air quality associated with reduced respiratory and allergic health among rural residents living in close proximity to a CAFO.

Airborne transmission may have played a role in the spread of 2015 highly pathogenic avian influenza outbreaks in the United States

The unprecedented 2015 outbreaks of highly pathogenic avian influenza (HPAI) H5N2 in the U.S. devastated its poultry industry and resulted in over $3 billion economic impacts. Today HPAI continues eroding poultry operations and disrupting animal protein supply chains around the world. Anecdotal evidence in 2015 suggested that in some cases the AI virus was aerially introduced into poultry houses, as abnormal bird mortality started near air inlets of the infected houses. This study modeled air movement trajectories and virus concentrations that were used to assess the probability or risk of airborne transmission for the 77 HPAI cases in Iowa. The results show that majority of the positive cases in Iowa might have received airborne virus, carried by fine particulate matter, from infected farms within the state (i.e., intrastate) and infected farms from the neighboring states (i.e., interstate). The modeled airborne virus concentrations at the Iowa recipient sites never exceeded the minimal infective doses for poultry; however, the continuous exposure might have increased airborne infection risks. In the worst-case scenario (i.e., maximum virus shedding rate, highest emission rate, and longest half-life), 33 Iowa cases had > 10% (three cases > 50%) infection probability, indicating a medium to high risk of airborne transmission for these cases. Probability of airborne HPAI infection could be affected by farm type, flock size, and distance to previously infected farms; and more importantly, it can be markedly reduced by swift depopulation and inlet air filtration. The research results provide insights into the risk of airborne transmission of HPAI virus via fine dust particles and the importance of preventative and containment strategies such as air filtration and quick depopulation of infected flocks.

Modeling emissions from CAFO poultry farms in Poland and evaluating potential risk to surrounding populations

The world-wide use of concentrated animal feeding operations (CAFOs) for livestock production demands the need to evaluate the potential impact to public health. We estimated the exposure of various airborne pollutants for populations residing in close proximity to 10 poultry CAFOs located in Central Poland. Ammonia (NH3), carbon dioxide (CO2), carbon monoxide (CO), hydrogen sulfide (H2S), methane (CH4), nitrogen dioxide (NO2), nitrous oxide (N2O), sulfur dioxide (SO2), and organic dust were the pollutants of interest for this study. Because no monitoring data were available, we used the steady-state Gaussian dispersion model AERMOD to estimate pollutant concentrations for the exposed population in order to calculate the hazard index (HI) for a combined mixture of chemicals. Our results indicate that while the levels of certain pollutants are expected to exceed background levels commonly found in the environment they did not result in calculated hazard indexes which exceeded unity suggesting low potential for adverse health effects for the surrounding community for the mixture of chemicals. The study was conducted through a cooperation between the Agency for Toxic Substances and Disease Registry (ATSDR) in the USA and the Nofer Institute of Occupational Medicine (NIOM) in Poland.