Assimilation of conventional and satellite wind observations in a mesoscale atmospheric model for studying atmospheric dispersion

A mesoscale atmospheric model PSU/NCAR MM5 is used to provide operational weather forecasts for a nuclear emergency response decision support system on the southeast coast of India. In this study the performance of the MM5 model with assimilation of conventional surface and upper-air observations along with satellite derived 2-d surface wind data from QuickSCAT sources is examined. Two numerical experiments with MM5 are conducted: one with static initialization using NCEP FNL data and second with dynamic initialization by assimilation of observations using four dimensional data assimilation (FDDA) analysis nudging for a pre-forecast period of 12 h. Dispersion simulations are conducted for a hypothetical source at Kalpakkam location with the HYSPLIT Lagrangian particle model using simulated wind field from the above experiments. The present paper brings out the differences in the atmospheric model predictions and the differences in dispersion model results from control and assimilation runs. An improvement is noted in the atmospheric fields from the assimilation experiment which has led to significant alteration in the trajectory positions, plume orientation and its distribution pattern. Sensitivity tests using different PBL and surface parameterizations indicated the simple first order closure schemes (Blackadar, MRF) coupled with the simple soil model have given better results for various atmospheric fields. The study illustrates the impact of the assimilation of the scatterometer wind and automated weather stations (AWS) observations on the meteorological model predictions and the dispersion results.     

Linear and non-linear relationships mapping the Henry’s law parameters of organic pesticides

This work aims to predict the air to water partitioning for 96 organic pesticides by means of the Quantitative Structure–Property Relationships Theory. After performing structural feature selection with Genetics Algorithms and Replacement Method linear approaches, it is found that among the most important molecular features appears the Moriguchi octanol–water partition coefficient, and higher lipophilicities would lead to compounds having higher Henry’s law constants. We also compare the statistical performance achieved by four fully-connected Feed-Forward Multilayer Perceptrons Artificial Neural Networks. The statistical results found reveal that the best performing model uses the Levenberg–Marquardt with Bayesian regularization (BR) weighting function for achieving the most accurate predictions.     

Seasonal monoterpene and sesquiterpene emissions from Pinus taeda and Pinus virginiana

Seasonal volatile organic compound emission data from loblolly pine (Pinus taeda) and Virginia pine (Pinus virginiana) were collected using branch enclosure techniques in Central North Carolina, USA. P. taeda monoterpene emission rates were at least ten times higher than oxygenated monoterpene and sesquiterpene emissions in all seasons. α-pinene and β-pinene were the most abundant emissions, while β-caryophyllene had the highest sesquiterpene emission rate from this species. β-phellandrene was the dominant compound emitted from P. virginiana, followed by the sesquiterpene β-caryophyllene. Sesquiterpene emissions from P. virginiana have not been reported in the literature previously. Summer sesquiterpene emissions from P. virginiana were nearly as high as monoterpene emissions, but were 4–12 times lower than monoterpene emissions in the other seasons. Oxygenated monoterpenes and 2-methyl-3-buten-2-ol were emitted at higher rates from P. taeda than from P. virginiana. Temperature response of the pinenes from P. taeda is similar to previously reported values used in emission models, while that for other compounds falls at the lower end of the previously reported range. Temperature response of all compounds from P. virginiana is in reasonable agreement with previously reported values from other pine species. There is evidence of light dependence of sesquiterpene emission after accounting for temperature response from both species. This effect is somewhat stronger in P. taeda. Bud break, needle expansion, and needle fall (and therefore wind events) seemed to increase monoterpene emission during non-summer seasons. In some instances springtime monoterpene emissions were higher than summertime emissions despite cooler temperatures. Emissions of individual compounds within monoterpene, oxygenated monoterpene, and sesquiterpene classes were highly correlated with each other. Compounds from different classes were much less correlated within each species. This is due to a varying temporal emission patterns for each BVOC class and suggests different production, storage, and emission controls for each. Analysis of enclosure blanks and diurnal patterns indicates that, despite precautions, disturbance due to the enclosure technique may still impact monoterpene emission rate estimates. This did not appear to affect sesquiterpene emissions.     

Analysis of 3-year observations of CFC-11, CFC-12 and CFC-113 from a semi-rural site in China

In-situ measurements of atmospheric chlorofluorocarbons (CFCs) can be used to the assess their global and regional emissions and to check for compliance with phase-out schedules under Montreal protocol and its amendments. The atmospheric mixing ratios of CFC-11 (CCl₃F), CFC-12 (CCl₂F₂) and CFC-113 (CCl₂F–CClF₂) have been measured by an automated in-situ GC-ECDs system at the regional Chinese Global Atmosphere Watch (GAW) station Shangdianzi (SDZ), from November 2006 to October 2009. The time series for these three principal CFCs showed large episodic events and background conditions occurred for approximately 30% (CFC-11), 52% (CFC-12) and 56% (CFC-113) of the measurements. The mean background mixing ratios for CFC-11, CFC-12 and CFC-113 were 244.8 ppt (parts per trillion, 10^?12, molar) 539.6 ppt and 76.8 ppt, respectively, for 2006–2009. The enhanced CFC mixing ratios compared to AGAGE sites such as Trinidad Head (THD), US and Mace Head (MHD), Ireland suggest regional influences even during background conditions at SDZ, which is much closer to highly-populated areas. Between 2006 and 2009 background CFCs exhibited downward trends at rates of ?2.0 ppt yr^?1 for CFC-11, ?2.5 ppt yr^?1 for CFC-12 and ?0.7 ppt yr^?1 for CFC-113. De-trended 3-year average background seasonal cycles displayed small fluctuations with peak-to-trough amplitudes of 1.0 ± 0.02 ppt (0.4%) for background CFC-11, 1.3 ± 2.1 ppt (0.3%) for CFC-12 and 0.2 ± 0.4 ppt (0.3%) for CFC-113. On the other hand, during pollution periods these CFCs showed much larger seasonal cycles of 11.2 ± 10.7 ppt (5%) for CFC-11, 7.5 ± 6.5 ppt (2%) for CFC-12 and 1.0 ± 1.2 ppt (1.2%) for CFC-113, with apparent winter minima and early summer maxima. This enhancement was attributed to prevailing wind directions from urban regions in summer and to enhanced anthropogenic sources during the warm season. In general, horizontal winds from northeast showed negative contribution to atmospheric CFCs loading, whereas South Western advection (urban sector: Beijing) had positive contributions.     

PM2.5 and PM10: The influence of sugarcane burning on potential cancer risk

In Brazil, sugarcane fields are often burned to facilitate manual harvesting, and this burning causes environmental pollution from the large amounts of soot released into the atmosphere. This material contains numerous organic compounds such as PAHs. In this study, the concentrations of PAHs in two particulate-matter fractions (PM_2.5 and PM₁₀) in the city of Araraquara (SE Brazil, with around 200,000 inhabitants and surrounded by sugarcane plantations) were determined during the sugarcane harvest (HV) and non-harvest (NHV) seasons in 2008 and 2009. The sampling strategy included four campaigns, with 60 samples in the NHV season and 220 samples in the HV season. The PM_2.5 and PM₁₀ fractions were collected using a dichotomous sampler (10 L min^?1, 24 h) with Teflon? filters. The filter sets were extracted (ultrasonic bath with hexane/acetone (1:1 v/v)) and analyzed by HPLC/Fluorescence. The median concentration for total PAHs (PM_2.5 in 2009) was 0.99 ng m^?3 (NHV) and 3.3 ng m^?3 (HV). In the HV season, the total concentration of carcinogenic PAHs (benz(a)anthracene, benzo(b)fluoranthene, benzo(k)fluoranthene, and benzo(a)pyrene) was 5 times higher than in the NHV season. B(a)P median concentrations were 0.017 ng m^?3 and 0.12 ng m^?3 for the NHV and HV seasons, respectively. The potential cancer risk associated with exposure through inhalation of these compounds was estimated based on the benzo[a]pyrene toxic equivalence (BaP_eq), where the overall toxicity of a PAH mixture is defined by the concentration of each compound multiplied by its relative toxic equivalence factor (TEF). BaP_eq median (2008 and 2009 years) ranged between 0.65 and 1.0 ng m^?3 and 1.2–1.4 ng m^?3 for the NHV and HV seasons, respectively. Considering that the maximum permissible BaP_eq in ambient air is 1 ng m^?3, related to the increased carcinogenic risk, our data suggest that the level of human exposure to PAHs in cities surrounded by sugarcane crops where the burning process is used is cause for concern.     

An urban emissions inventory for South America and its application in numerical modeling of atmospheric chemical composition at local and regional scales

This work describes the development of an urban vehicle emissions inventory for South America, based on the analysis and aggregation of available inventories for major cities, with emphasis on its application in regional atmospheric chemistry modeling. Due to the limited number of available local inventories, urban emissions were extrapolated based on the correlation between city vehicle density and mobile source emissions of carbon monoxide (CO) and nitrogen oxides (NOx). Emissions were geographically distributed using a methodology that delimits urban areas using high spatial resolution remote sensing products. This numerical algorithm enabled a more precise representation of urban centers. The derived regional inventory was evaluated by analyzing the performance of a chemical weather forecast model in relation to observations of CO, NOx and O₃ in two different urban areas, São Paulo and Belo Horizonte. The gas mixing ratios simulated using the proposed regional inventory show good agreement with observations, consistently representing their hourly and daily variability. These results show that the integration of municipal inventories in a regional emissions map and their precise distribution in fine scale resolutions are important tools in regional atmospheric chemistry modeling.     

Estimation of economic costs of particulate air pollution from road transport in China

Valuation of health effects of air pollution is becoming a critical component of the performance of cost–benefit analysis of pollution control measures, which provides a basis for setting priorities for action. Beijing has focused on control of transport emission as vehicular emissions have recently become an important source of air pollution, particularly during Olympic games and Post-games. In this paper, we conducted an estimation of health effects and economic cost caused by road transport-related air pollution using an integrated assessment approach which utilizes air quality model, engineering, epidemiology, and economics. The results show that the total economic cost of health impacts due to air pollution contributed from transport in Beijing during 2004–2008 was 272, 297, 310, 323, 298 million US$ (mean value), respectively. The economic costs of road transport accounted for 0.52, 0.57, 0.60, 0.62, and 0.58% of annual Beijing GDP from 2004 to 2008. Average cost per vehicle and per ton of PM₁₀ emission from road transport can also be estimated as 106 US $/number and 3584 US $ t^?1, respectively. These findings illustrate that the impact of road transport contributed particulate air pollution on human health could be substantial in Beijing, whether in physical and economic terms. Therefore, some control measures to reduce transport emissions could lead to considerable economic benefit.     

Influence of roads and inhabited areas on metal concentrations in terrestrial mosses

The guidelines regarding sampling terrestrial mosses for biomonitoring the atmospheric contamination in a region specifically recommend that sampling close to focal points of contamination (i.e. roads, populated areas, etc.) should be avoided for satisfactory characterization of general patterns of contamination. However, these recommendations cannot always be followed in many parts of the world with dense, highly dispersed populations. The aim of the present study was to determine how the distances between sampling sites (SS) and contamination foci such as roads, isolated houses and urban nuclei affect regular, large-scale sampling networks. Metal concentrations obtained in biennial sampling surveys carried out between 2000 and 2006 in Galicia (NW Spain) were used, and the relative proportions of the concentrations corresponding to small-scale and large-scale processes were calculated. The possible relationship between the mean concentrations corresponding to small-scale processes in the different sampling surveys and the distances from SS to roads, buildings and urban nuclei was established by a GIS. Outliers in the data were identified at many of the SS and frequency statistics revealed the absence of any relationship between the distance to these foci and the concentrations of metals in the moss. The method also revealed many instances of the influence of one or more small-scale contamination foci such as airports and railways (often not considered in the sampling recommendations) on SS. Relocation of such SS would improve the representativeness of the sampling grid.     

Machine learning for radioxenon event classification for the Comprehensive Nuclear-Test-Ban Treaty

A method of weapon detection for the Comprehensive nuclear-Test-Ban-Treaty (CTBT) consists of monitoring the amount of radioxenon in the atmosphere by measuring and sampling the activity concentration of ^131mXe, ¹³³Xe, ^133mXe, and ¹³⁵Xe by radionuclide monitoring. Several explosion samples were simulated based on real data since the measured data of this type is quite rare. These data sets consisted of different circumstances of a nuclear explosion, and are used as training data sets to establish an effective classification model employing state-of-the-art technologies in machine learning. A study was conducted involving classic induction algorithms in machine learning including Naïve Bayes, Neural Networks, Decision Trees, k-Nearest Neighbors, and Support Vector Machines, that revealed that they can successfully be used in this practical application. In particular, our studies show that many induction algorithms in machine learning outperform a simple linear discriminator when a signal is found in a high radioxenon background environment.