Use of CALPUFF to predict airborne Mn levels at schools in an urban area impacted by a nearby manganese alloy plant

Applications of dynamic simulation for source analysis of soil pollutants based on atmospheric diffusion and deposition model

Existing receptor-model-based source apportionment methods failed to derive source contributions to accumulation of soil heavy metals (SHMs). In this research, a dynamics-simulation-based source apportionment approach (DSSA) was developed by integrating mathematical models of source release, diffusion and deposition pathway, and receptor accumulation, to quantify accumulative contributions of SHMs. The case study was carried out in a complex industrialized region in southeast China to investigate pollution situation of SHMs (Zn, Pb, Ni, As, Cd, and Cr). The results showed that SHMs distributions were affected by seasonal variation and near-surface meteorology, which could be sequenced by correlation coefficient as temperature (0.968) > humidity (0.552) > precipitation (0.389) > wind speed (0.386). The source categories and corresponding contribution rates were identified as: i) battery plant to Zn (72.32%) and Pb (71.73%), ii) traffic to Ni (64.55%), iii) traffic and agriculture to Cd (43.26%, 41.63%), iv) agriculture to As (75.30%) and Cr (60.05%), which was similar to the results of positive matrix factorization (PMF). Furthermore, DSSA could illustrate SHMs migration process from source to receptor. The uncertainty analysis further proved the distinct advantages of DSSA. The results of this research could predict pollutant enrichment and could provide new perspective for environment and public health management.

AERMOD modeling of ambient manganese for residents living near a ferromanganese refinery in Marietta, OH, USA

Elevated exposure to ambient manganese (Mn) is associated with adverse health outcomes. In Marietta, Ohio, the primary source of ambient Mn exposure is from the longest operating ferromanganese refinery in North America. In this study, the US EPA air dispersion model, AERMOD, was used to estimate ambient air Mn levels near the refinery for the years 2008-2013. Modeled air Mn concentrations for 2009-2010 were compared to concentrations obtained from a stationary air sampler. Census block population data were used to estimate population sizes exposed to an annual average air Mn > 50 ng/m3, the US EPA guideline for chronic exposure, for each year. Associations between modeled air Mn, measured soil Mn, and measured indoor dust Mn in the modeled area were also examined. Median modeled air Mn concentrations ranged from 6.3 to 43 ng/m3 across the years. From 12,000-56,000 individuals, including over 2000 children aged 0-14 years, were exposed to respirable annual average ambient air Mn levels exceeding 50 ng/m3 in five of the six years. For 2009-2010, the median modeled air Mn concentration at the stationary site was 20 ng/m3, compared to 18 ng/m3 measured with the stationary air sampler. All model performance measures for monthly modeled concentrations compared to measured concentrations were within acceptable limits. The study shows that AERMOD modeling of ambient air Mn is a viable method for estimating exposure from refinery emissions and that the Marietta area population was at times exposed to Mn levels that exceeded US EPA guidelines.

A Review on the Environmental Exposure to Airborne Manganese, Biomonitoring, and Neurological/Neuropsychological Outcomes

The occupational exposure to airborne manganese (Mn) has been linked for decades with neurological effects. With respect to its environmental exposure, the first reviews on this matter stated that the risk posed to human health by this kind of exposure was still unknown. Later, many studies have been developed to analyze the association between environmental Mn exposure and health effects, most of them including the measure of Mn in selected human biomarkers. This review aims at collecting and organizing the literature dealing with the environmental airborne Mn exposure (other routes of exposure were intentionally removed from this review), the biomonitoring of this metal in different body matrices (e.g., blood, urine, nails, hair), and the association between exposure and several adverse health effects, such as, e.g., neurocognitive, neurodevelopmental, or neurobehavioral outcomes. From the different exposure routes, inhalation was the only one considered in this review, to take into account the areas influenced by industrial activities closely related to the Mn industry (ferromanganese and silicomanganese plants, Mn ore mines, and their processing plants) and by traffic in countries where a fuel additive, methylcyclopentadienyl manganese tricarbonyl (MMT), has been used for years. In these areas, high air Mn levels have been reported in comparison with the annual Reference Concentration (RfC) given by the US EPA for Mn, 50 ng/m³. This review was performed using Scopus and MEDLINE databases with a keyword search strategy that took into account that each valid reference should include at least participants that were exposed to environmental airborne Mn and that were subjected to analysis of Mn in biomarkers or subjected to neurological/neuropsychological tests or both. Overall, 47 references matching these criteria were included in the discussion. Most of them report the measure of Mn in selected biomarkers (N = 43) and the assessment of different neurological outcomes (N = 31). A negative association is usually obtained between Mn levels in hair and some neurological outcomes, such as cognitive, motor, olfactory, and emotional functions, but not always significant. However, other biomarkers, such as blood and urine, do not seem to reflect the chronic environmental exposure to low/moderate levels of airborne Mn. Further studies combining the determination of the Mn exposure through environmental airborne sources and biomarkers of exposure and the evaluation of at least cognitive and motor functions are needed to better understand the effects of chronic non-occupational exposure to airborne Mn.

Odour impact assessment by considering short-term ambient concentrations: A multi-model and two-site comparison

Short-term events are one of the specific aspects that differentiate odour nuisance problems from conventional air quality pollutants. Atmospheric dispersion modelling has been considered the gold standard to realise odour impact assessments and to calculate separation distances. Most of these models provide predictions of concentrations of a pollutant in ambient air on an hourly basis. Even when the hourly mean odour concentration is lower than the perception threshold, concentration peaks above the threshold may occur during this period. The constant peak-to-mean factor is nowadays the most widespread method for evaluating short-term concentrations from the long-term ones. Different approaches have been proposed in the scientific literature to consider non-constant peak-to-mean factors. Two prominent approaches to do so are the i) variable peak-to-mean factor which considers the distance from the source and atmospheric stability and the ii) concentration-variance transport. In this sense, the aim of this work is to compare the results of three different freely available dispersion models (namely, CALPUFF, LAPMOD and GRAL), which implement three distinct ways to evaluate the short-term concentration values. Two sites, one in Austria and the other in Italy, were selected for the investigation. Dispersion model results were compared and discussed both in terms of long-term (hourly) concentrations and short-term. An important outcome of this work is that the dispersion models provided more equivalent results for hourly mean concentrations, in particular in the far-field. On the contrary, the method to evaluate short-term concentrations can deliver disparate results, thereby revealing a potential risk of poor assessment conclusions. The utilistion of a multiangle methodological approach (dispersion models, study site locations, algorithms to incorporate short-term concentrations) allowed providing useful information for future studies and policymaking in this field. Accordingly, our findings call for awareness on how the use of a particular dispersion model and its sub-hourly peak calculation method can affect odour impact assessment conclusions and compliance demonstrations.