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Odor pollution is the biggest source of complaints from citizens concerning environmental issues after noise. Often, the need for corrective actions is evaluated through simulations performed with atmospheric dispersion models. To save resources, air pollution control institutions perform a first-level odor impact assessment, for screening purposes. This is often based on Gaussian dispersion models (GDM), which does not need high computational power. However, their outputs tend to be conservative regarding the analyzed situation, rather than representative of the real in-site conditions. Hence, regulations and guidelines adopted at an institutional level for authorization/control purposes are based on Lagrangian particle dispersion models (LPDM). These models grant a more accurate simulation of the pollutants' dispersion even if they are more demanding regarding both technical skills and computing power. The present study aims to increase the accuracy of screening odor impact assessment by identifying the correlation function of the outputs derived from the two simulation models. The case study is placed in northern Italy, where a single-point source, with various stack heights, was considered. The case study is placed in northern Italy, where a single-point source, with various stack heights, was considered. The obtained correlation functions allow the practitioner to have a more accurate first-level odor impact assessment, to save time for training, and to reduce the site-specific meteorological data before proceeding with the simulation. The identified functions could allow institutions to estimate the results that would have been forecasted with the application of the more complex LPDM, applying, however, the much simpler GDM. This solution grants an accurate tool which can be used to address citizens' concerns while saving workforce and technical resources. Limitations are related to the specificity of the method regarding type sources, orography, and meteorological conditions. Comparison with other screening tools is also presented and discussed.

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Innovations in aerobiological and epidemiological modeling are enabling the development of powerful techniques to infer connectivity networks for transboundary pathogens in ways that were not previously possible. The innovations are supported by improved access to historical and near real-time highly resolved weather data, multi-country disease surveillance data, and enhanced computing power. Using wheat rusts as an exemplar, we introduce a flexible modeling framework to identify characteristic pathways for long-distance spore dispersal within countries and beyond national borders. We show how the models are used for near real-time early warning systems to support smallholder farmers in East Africa and South Asia. Wheat rust pathogens are ideal exemplars because they continue to pose threats to food security, especially in regions of the world where resources for control are limited. The risks are exacerbated by the rapid appearance and spread of new pathogenic strains, prodigious spore production, and long-distance dispersal for transboundary and pandemic spread.

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A growing constellation of satellites is providing near-global coverage of column-averaged CO2 observations. Launched in 2019, NASA's OCO-3 instrument is set to provide XCO2 observations at a high spatial and temporal resolution for regional domains (100 × 100 km). The atmospheric column version of the Stochastic Time-Inverted Lagrangian Transport (X-STILT) model is an established method of determining the influence of upwind sources on column measurements of the atmosphere, providing a means of analysis for current OCO-3 observations and future space-based column-observing missions. However, OCO-3 is expected to provide hundreds of soundings per targeted observation, straining this already computationally intensive technique. This work proposes a novel scheme to be used with the X-STILT model to generate upwind influence footprints with less computational expense. The method uses X-STILT generated influence footprints from a key subset of OCO-3 soundings. A nonlinear weighted averaging is applied to these footprints to construct additional footprints for the remaining soundings. The effects of subset selection, meteorological data, and topography are investigated for two test sites: Los Angeles, California, and Salt Lake City, Utah. The computational time required to model the source sensitivities for OCO-3 interpretation was reduced by 62% and 78% with errors smaller than other previously acknowledged uncertainties in the modeling system (OCO-3 retrieval error, atmospheric transport error, prior emissions error, etc.). Limitations and future applications for future CO2 missions are also discussed.

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To curb the spread of the coronavirus, China implemented lockdown policies on January 23, 2020. The resulting extreme changes in human behavior may have influenced the air pollutants concentration. However, despite these changes, hazy weather persisted in Shanghai and became a public issue. This study aims to investigate air pollutant mass concentration changes during the lockdown in Shanghai. Air pollutant mass concentration data and meteorological data during the pre-lockdown period and the level I response lockdown period were analyzed by statistical analysis and a Lagrangian particle diffusion model. The data was classified in three periods: P1 (pre-lockdown: 10 days before the Spring Festival), P2 (the first 10 days after lockdown: during the Spring Festival celebration), and P3 (the second 10 days after lockdown: after the Spring Festival). Data for the same period in 2019 were used as a reference. The results indicate that the Spring Festival holiday in 2019 resulted in a reduction in energy consumption, which led to a decrease in PM2.5 (26.4%) and NO2 (43.41%) mass concentration, but an increase in ozone mass concentration (31.39%) in P2 compared with P1. The integrated effect of the Spring Festival holiday and lockdown in 2020 resulted in a decrease in PM2.5 (36.5%) and NO2 (51.9%) mass concentrations, but an increase in ozone mass concentration (43.8%) in P2 compared with P1. After the Spring Festival, the mass concentrations of PM2.5, SO2, and NO2 increased by 74.41%, 5.52%, and 53.28%, respectively in P3 compared with P2 in 2019. However, PM2.5 and SO2 concentrations in 2020 continued to decrease, by 14.74% and 4.61%, respectively, while NO2 mass concentration increased by 7.82% in P3 compared with P2. We also found that PM2.5 mass concentration is susceptible to regional transmission from the surrounding cities. PM2.5 and other gaseous pollutants show different correlations in different periods, while NO2 and O3 always show a strong negative correlation. The principal components before the Spring Festival in 2019 were O3 and NO2, and after the Spring Festival, they were PM2.5 and CO, while the principal components before the lockdown in 2020 were PM2.5 and CO, and during lockdown they were O3 and NO2.

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The construction of Akkuyu Nuclear Power Plant (NPP) was launched in 2018 and the plant is expected to be operative by the year 2023. Being situated in the Mediterranean coastline, Akkuyu NPP will be the first nuclear power generation facility in Turkey. The plant will have four Russian VVER-1200 type pressurized water reactors with a total installed capacity of 4800 MW. In this study, atmospheric dispersion and ground level deposition of Cs-137 and I-131 released from a possible accident in Akkuyu NPP was estimated using a Lagrangian particle dispersion model, FLEXPART, for different time periods representing relatively extreme meteorological conditions for Mersin. The source term used in simulations was assumed the same with that of the Chernobyl NPP accident that occurred in 1986. In addition, cumulative dose and risk values were calculated from FLEXPART output datasets considering potential exposure pathways such as inhalation, ground-shine exposure and cloud-shine exposure. The results were further analyzed with python codes and dose and risk maps were created for local and regional scales. According to results of the study, it was found that the vicinity of Mersin and Central Anatolia were simulated to be the most significantly affected areas from the accident under both scenario conditions. The northern and western parts and all coastlines of Turkey were simulated to be more contaminated in the simulations conducted under December 2009 conditions, whereas southern and western parts of Turkey and some parts of Middle East countries like Syria, Iraq and Lebanon were simulated to be comparatively more contaminated under August 2010 conditions. The results indicated that radioactivity levels exceeding 100 kBq/m2 were observed near the accident site under both scenario conditions. Values exceeding 10 kBq/m2 level were simulated in western Turkey in the first scenario whereas similar values were found in eastern Turkey in the second scenario. Furthermore, the results indicated 7-day thyroid dose values ranging between 0.10 mSv and 10.0 mSv in western and eastern parts of Mediterranean region for the first and the second scenario, respectively. Similarly, 1-year effective dose of only Cs-137 ranged between 0.1 mSv and 1.0 mSv around Akkuyu NPP site in both scenarios. The results revealed that meteorological conditions were among the most important parameter for the fate and transport of radioactivity originating from such a catastrophic event.

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Site specific radionuclide dispersion databases were archived for the emergency response to the hypothetical releases of 137Cs from the Uljin nuclear power plant in Korea. These databases were obtained with the horizontal resolution of 1.5 km in the local domain centered the power plant site by simulations of the Lagrangian Particle Dispersion Model (LPDM) with the Unified Model (UM)-Local Data Assimilation Prediction System (LDAPS). The Eulerian Dispersion Model-East Asia (EDM-EA) with the UM-Global Data Assimilation Prediction System (UM-GDAPS) meteorological models was used to get dispersion databases in the regional domain. The LPDM model was performed for a year with a 5-day interval yielding 72 synoptic time-scale cases in a year. For each case hourly mean near surface concentrations, hourly mean column integrated concentrations, hourly total depositions for 5 consecutive days were archived by the LPDM model in the local domain and by the EDM-EA model in the regional domain of Asia. Among 72 synoptic cases in a year the worst synoptic case that showed the highest mean surface concentration averaged for 5 days in the LPDM model domain was chosen to illustrate the emergency preparedness to the hypothetical accident at the site. The simulated results by the LPDM model with the 137Cs emission rate of the Fukushima nuclear power plant accident for the first 5-day period were found to be able to provide prerequisite information for the emergency response to the early phase of the accident whereas those of the EDM-EA model could provide information required for the environmental impact assessment of the accident in the regional domain. The archived site-specific database of 72 synoptic cases in a year could have a great potential to be used as a prognostic information on the emergency preparedness for the early phase of accident.

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A methodology for the estimation of the emission rate of 137Cs by the Lagrangian Particle Dispersion Model (LPDM) with the use of monitored 137Cs concentrations around a nuclear power plant has been developed. This method has been employed with the MM5 meteorological model in the 600 km × 600 km model domain with the horizontal grid scale of 3 km × 3 km centered at the Fukushima nuclear power plant to estimate 137Cs emission rate for the accidental period from 00 UTC 12 March to 00 UTC 6 April 2011. The Lagrangian Particles are released continuously with the rate of one particle per minute at the first level modelled, about 15 m above the power plant site. The presently developed method was able to simulate quite reasonably the estimated 137Cs emission rate compared with other studies, suggesting the potential usefulness of the present method for the estimation of the emission rate from the accidental power plant without detailed inventories of reactors and fuel assemblies and spent fuels. The advantage of this method is not so complicated but can be applied only based on one-time forward LPDM simulation with monitored concentrations around the power plant, in contrast to other inverse models. It was also found that continuously monitored radionuclides concentrations from possibly many sites located in all directions around the power plant are required to get accurate continuous emission rates from the accident power plant. The current methodology can also be used to verify the previous version of radionuclides emissions used among other modeling groups for the cases of intermittent or discontinuous samplings.

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