A GIS-based atmospheric dispersion model for pollutants emitted by complex source areas

Assessing the spatial distribution of odor at an urban waterfront using AERMOD coupled with sensor measurements

Impressions of a place are partly formed by smell. The urban waterfronts often leave a rather poor impression due to odor pollution, resulting in recurring complaints. The nature of such complaints can be subjective and vague, so there is a growing interest in quantitative measurements of emissions to explore the causes of malodorous influence. In the present work, an air quality monitor with an H2S sensor was employed to continuously measure emissions of malodors at 1-min resolution. H2S is often considered to be the predominant odorous substance from sludge and water bodies as it is readily perceptible. The integrated means of concentration from in situ measurements were combined with the AERMOD dispersion model to reveal the spatial distribution of odor concentrations and estimate the extent of odor-prone areas at a daily time step. Year-long observations showed that the diurnal profile exhibits a positively skewed distribution. Meteorology plays a vital role in odor dispersion; the degree of dispersion was explored on a case-by-case basis. There is a greater likelihood of capturing the concentration peaks at night (21:00 to 6:00) as the air is more stable then with less tendency for vertical mixing but favors a horizontal spread. This study indicates that malodors are changeable in time and space and establishes a new approach to using H2S sensor data and resolves a long-standing question about odor in Hong Kong.Implications: this study establishes a new approach combining dispersion model with novel H2S sensor data to understand the characteristics and pattern of odor emanated from the urban waterfront in Hong Kong. The sensor has dynamic concentration range to detect the episodic level of H2S and low level at background conditions. It provides more complete information in relation to odor annoyance, as well as quantitative information useful for odor regulation.

Towards quantifying atmospheric dispersion of pesticide spray drift in Yuma County Arizona

While pesticide vapor and particles from agricultural spray drift have been reported to pose a risk to public health, limited baseline ambient measurements exist to warrant an accurate assessment of their impacts at community-to-county-wide scale. Here, we present an initial modeling investigation of the transport and deposition of applied pesticides in an agricultural county in Arizona (Yuma County), to provide initial estimates on the corresponding enhancements in ambient levels of these spray drifts downwind of application sites. With a 50 × 50 km domain, we use the dispersion model CALPUFF with meteorology from the Weather Research and Forecasting (WRF) to investigate the spatiotemporal distribution of pesticide abundance due to spray drift from a representative sample of nine application sites. Data records for nine application days in September and October 2011, which are the peak months of pesticide application, were retroactively simulated for 48-h for all nine application sites using an active ingredient lambda-cyhalothrin, which is a commonly-used pesticide in the county. Twenty-one WRF/CALPUFF simulations were conducted with varying emissions, chemical lifetime, deposition rate, application height, and meteorology inputs, allowing for an ensemble-based analysis on the possible ranges in modeled abundance. Our results show that dispersion of vapors released at time of application heavily depends on prevailing meteorology, particularly wind speed and direction. Dispersion is limited to thin plumes that are easily transported out of the domain. The ensemble-mean vapor concentrations of the 48-h average (> 90 percentile domain-wide) range from 0.2 nanograms (ng)/m3 to 200 ng/m3, and the peak can be as high as 1000 ng/m3 near the application sites. Pesticide particles are mainly deposited within 1-2 km from the application sites at an average rate of 106 ng/km2/h but vary with particle mean diameter and standard deviation. While these findings are generally consistent with reported ambient levels in the literature, the associated ensemble-spread on these estimates are in the same order of magnitude as their ensemble-mean. At the two nearby communities downwind of these sites, we find that peak vapor concentrations are less than 50 ng/m3 with exposure times of less than an hour, as approximately 99.4% of the vapors are advected out and 99.5% of the particles deposit within the domain. Results of this study indicate pesticide spray drift from a sample of application sites and representative days in Fall may have a limited impact on neighboring communities. However, we strongly suggest that field measurements should be collected for model validation and more rigorous investigation of the actual scale of these impacts when the bulk of pesticide applications across the county, variation in active pesticide ingredients, and potential resuspension of deposited particles are considered.

Grid computing method for atmospheric environmental capacity coupled with ventilation coefficient using CALPUFF simulation and GIS spatial analysis technology

Atmospheric environmental issues have evolved from point source pollution to regional pollution, leading to controlling specific air pollutant emissions. A-value method has been found suitable for estimating large-scale atmospheric environmental capacity rather than small-scale, resulting in the inaccuracy of developing air pollution control strategy. This study proposed a grid computing method based on the CALPUFF modelling system and GIS spatial analysis tool. The meteorological data from the MM5 model were used to simulate the spatial distribution of air pollutants. The meteorological flow field data was used to simulate the ventilation coefficient. The A value was revised with the simulated to achieve accurate results of atmospheric environment capacity. The credibility was verified by applying this method to Fengtai District, Beijing, China. The research area was divided into small partitions via the ArcGIS spatial analysis tool. The simulation results agreed well with the observation data from actual monitoring stations, even for the PM10 concentration with the most significant error (MRE: 7.05%-13.28%, RMSE: 11.62-17.89, R2: 0.84-0.90). The GIS spatial analysis tools were applied to match the underlying surface types and overcome the restrictions of administrative boundary management. The study proposed four schemes to achieve differentiated air pollutant emission reduction and develop suitable control strategies. Furthermore, this method can be applied on different scales of natural geographic boundaries and realize the precise spatial management of atmospheric environmental capacity.

Improved Gaussian plume model for atmospheric dispersion considering buoyancy and gravitational deposition: The case of multi-form tritium

Various types of radionuclides have different atmospheric dispersion characteristics, such as buoyancy and gravitational deposition phenomenon of light gas and heavy particles, respectively. Gaussian plume model was widely used to describe atmospheric dispersion behaviors of radioactive effluents, particularly for the purpose of engineering environmental impact assessment or nuclear emergency support. Nonetheless, buoyancy and gravitational deposition were rarely reported in previous work for tritium in particular, which might cause a deviation in evaluating near-surface concentration distribution and radiation dose to the public. Based on the multi-form tritium case, we made a quantitative description for the buoyancy and gravitational deposition phenomenon and discussed the feasibility of developing an improved Gaussian plume model to predict near-surface concentration distribution. Firstly, tritium concentration distribution near to the surface was predicted by using computational fluid dynamics method (CFD) and standard Gaussian plume model to reach consistency without consideration of buoyancy and gravitational deposition effects. Secondly, effects of buoyancy and gravitational deposition were identified by species transport model for gaseous tritium and discrete phase model for droplet tritium with integrating the buoyancy force caused by density variation of gaseous tritium and gravitational force of droplet tritium with enough size. Thirdly, buoyancy and gravitational deposition correction factors were obtained to modify the standard Gaussian plume model. Lastly, predictive results by improved Gaussian plume model were compared with CFD method. It was proved the improved correction method enables higher accuracy in predicting the atmospheric concentration distribution of gaseous pollutants with density variation or particles with gravitational deposition properties.

Assessment of gas dispersion near an operating landfill treated by different intermediate covers with soil alone, low-density polyethylene (LLDPE), or ethylene vinyl alcohol (EVOH) geomembrane

This study evaluated the impact area of odiferous gas (i.e., H₂S) dispersion near an operating landfill installed with three different intermediate covers, including soil alone, linear low-density polyethylene (LLDPE), or ethylene vinyl alcohol (EVOH) geomembrane (GM). By using the finite element method employing Reynolds-averaged Navier-Stokes and Fick's Law coupled equations, the performance of the different cover cases for reducing odor dispersion was comparatively evaluated considering environmental factors, including topographic, meteorology, and gas emission. The odor dispersion patterns and the size of affected residents were analyzed for the twelve different scenarios varied with the cover type and seasonal variation. According to the results, it was found that the wind speed affected the time of odor dispersions more with the relatively flat terrain conditions around the landfill but barely affected the size of the dispersion area. Moreover, it was found that the higher concentration (100 ppb) of odor gas is mainly located within a 5.0-km distance from the landfill. Among four seasons, the odor covers the largest area in summer, which is mainly due to the landfill producing more odor gas and giving a higher source concentration in summer. The gas dispersion simulation for different covers showed that the type of covering layer significantly affects the impact area boundary of gas odor. The results showed that the odor area of the LLDPE GM cover case is 1.3% of soil alone case, and the case of EVOH GM is 14.5% of LLDPE GM case. At the same time, the number of residents that may be affected by the odor of the LLDPE GM case and EVOH GM case is 4.81% and 0.63% of soil alone case, respectively.

Risk of Amyotrophic Lateral Sclerosis and Exposure to Particulate Matter from Vehicular Traffic: A Case-Control Study

(1) Background: Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease with still unknown etiology. Some occupational and environmental risk factors have been suggested, including long-term air pollutant exposure. We carried out a pilot case-control study in order to evaluate ALS risk due to particulate matter with a diameter of ≤10 µm (PM₁₀) as a proxy of vehicular traffic exposure. (2) Methods: We recruited ALS patients and controls referred to the Modena Neurology ALS Care Center between 1994 and 2015. Using a geographical information system, we modeled PM₁₀ concentrations due to traffic emissions at the geocoded residence address at the date of case diagnosis. We computed the odds ratio (OR) and 95% confidence interval (CI) of ALS according to increasing PM₁₀ exposure, using an unconditional logistic regression model adjusted for age and sex. (3) Results: For the 132 study participants (52 cases and 80 controls), the average of annual median and maximum PM₁₀ concentrations were 5.2 and 38.6 µg/m³, respectively. Using fixed cutpoints at 5, 10, and 20 of the annual median PM₁₀ levels, and compared with exposure <5 µg/m³, we found no excess ALS risk at 5-10 µg/m³ (OR 0.87, 95% CI 0.39-1.96), 10-20 µg/m³ (0.94, 95% CI 0.24-3.70), and ≥20 µg/m³ (0.87, 95% CI 0.05-15.01). Based on maximum PM₁₀ concentrations, we found a statistically unstable excess ALS risk for subjects exposed at 10-20 µg/m³ (OR 4.27, 95% CI 0.69-26.51) compared with those exposed <10 µg/m³. However, risk decreased at 20-50 µg/m³ (OR 1.49, 95% CI 0.39-5.75) and ≥50 µg/m³ (1.16, 95% CI 0.28-4.82). ALS risk in increasing tertiles of exposure showed a similar null association, while comparison between the highest and the three lowest quartiles lumped together showed little evidence for an excess risk at PM₁₀ concentrations (OR 1.13, 95% CI 0.50-2.55). After restricting the analysis to subjects with stable residence, we found substantially similar results. (4) Conclusions: In this pilot study, we found limited evidence of an increased ALS risk due to long-term exposure at high PM₁₀ concentration, though the high statistical imprecision of the risk estimates, due to the small sample size, particularly in some exposure categories, limited our capacity to detect small increases in risk, and further larger studies are needed to assess this relation.

Integrated system for population dose calculation and decision making on protection measures in case of an accident with air emissions in a nuclear power plant

The accidents in Chernobyl and Fukushima remind us that nuclear power plants should continuously invest resources in improving safety and in risk management. This paper presents the methodology for developing a measuring and modelling system with a high degree of automation, which enables predicting the effects of the spreading of radionuclides from the nuclear power plant to the atmosphere. The end result is the calculated population doses in the event of an accidental release, which is an essential piece of information needed by first responders to take proper action. The key challenge addressed by this methodology is how to build a system so that its operation is maximally automated, ongoing and in real time. Moreover, in a way that "fresh", normalized results for the hypothetically most probable types of emissions are always available to operators. The principle that normalized, fresh results are always automatically available to operators is the only real assurance that they will almost surely be available in the event of an accident and panic. This way, we can avoid performing complex model calculations at the operator's request when the accident is already taking place. The methodology divides the building of the system into key modules, which are substantiated and described. The theoretical section is followed by a description of implementation on the example of the Measuring and Modelling System at the Krško Nuclear Power Plant (in Slovenia). The system has been tested in regular nuclear emergency exercises and rated excellent by IAEA inspections; it has been operating automatically, continuously and in real time for many years. The availability of automatic results is counted for the last two years. Measurements and diagnostic modelling results were available for more than 96% and forecasts were available in more than 91% of all half-hour intervals.