Hybrid Single-Particle Lagrangian Integrated Trajectory Research Articles

Validation of the Atmospheric Dispersion Model NAME against Long-Range Tracer Release Experiments

Abstract The Met Office’s atmospheric dispersion model Numerical Atmospheric-Dispersion Modeling Environment (NAME) is validated against controlled tracer release experiments, considering the impact of the driving meteorological data and choices in the parameterization of unresolved motions. The Cross-Appalachian Tracer Experiment (CAPTEX) and Across North America Tracer Experiment (ANATEX) were long-range dispersion experiments in which inert tracers were released and the air concentrations measured across North America in the 1980s. NAME simulations of the experiments have been driven by both reanalysis meteorological data from European Centre for Medium-Range Weather Forecasts (ECMWF) and data from the Advanced Research version of the Weather Research and Forecasting (WRF) Model. NAME predictions of air concentrations are assessed against the experimental measurements, using a ranking method composed of four statistical parameters. Differences in the performance of NAME according to this ranking method are compared when driven by different meteorological sources. The effect of changing parameter values in NAME for the unresolved mesoscale motions parameterization is also considered, in particular, whether the parameter values giving the best performance rank are consistent with values typically used. The performance ranks are compared with analyses in the literature for other particle dispersion models, namely, Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT), Stochastic Time-Inverted Lagrangian Transport (STILT), and Flexible Particle (FLEXPART). It is found that NAME performance is comparable to the other dispersion models considered, with the different models responding similarly to differences in driving meteorological data.

Long-Range Transport Analysis Based on Eastern Atmospheric Circulation and Its Impact on the Dust Event over Moldavia, Romania in August 2022

During the second half of August 2022, a dust intrusion event occurred when dust that originated in the dry regions of the Kalmyk steppe (located in Russia, northeast of the Black Sea, north of Georgia, and northwest of the Caspian Sea) and the Precaspian plain was transported over the eastern region of Romania. The arid soil found in these areas can be attributed to an extended period of intense drought, with notable instances occurring in 2002, 2003, 2015, and 2018. This situation was further intensified by heatwaves experienced in May and June of 2022. The dust event was captured in MODIS images. In addition, smoke trains originating from fires in the north of the Azov were detected, but these did not reach Romania. Optical parameters from AERONET were used to confirm the dust event. To determine the trajectory of the particles, the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model was used in this paper. The ensemble median model was used to highlight the presence and concentration of dust in the eastern part of Romania. Aerosols were detected between 0 and 4 km, according to radar and ceilometer data from the REXDAN cloud remote sensing facility in Galați, Romania. This dust intrusion event was the result of the dominant easterly circulation caused by the extension of the East European High to the northeast of the continent, which transported the dust towards the eastern part of Romania for more than 2 days. Moreover, the torrential rains between 22 and 24 August did not clear the atmosphere of dust, since the intense easterly circulation kept carrying the dust into the Moldavian area.

Modeling the Seasonal Variation of Windborne Transmission of Porcine Reproductive and Respiratory Syndrome Virus between Swine Farms.

Modeling the windborne transmission of aerosolized pathogens is challenging. We adapted an atmospheric dispersion model named the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model to simulate the windborne dispersion of porcine reproductive and respiratory syndrome virus (PRRSv) between swine farms and incorporated the findings into an outbreak investigation. The risk was estimated semi-quantitatively based on the cumulative daily deposition of windborne particles and the distance to the closest emitting farm with an ongoing outbreak. Five years of data (2014:2018) were used to study the seasonal differences of the deposition thresholds of the airborne particles containing PRRSv and to evaluate the model in relation to risk prediction and barn air filtration. When the 14-day cumulative deposition was considered, in winter, above-threshold particle depositions would reach up to 30 km from emitting farms with 84% of them being within 10 km. Long-distance pathogen transmission was highest in winter and fall, lower in spring, and least in summer. The model successfully replicated the observed seasonality of PRRSv, where fall and winter posed a higher risk for outbreaks. Reaching the humidity and temperature thresholds tolerated by the virus in spring and summer reduced the survival and infectivity of aerosols beyond 10-20 km. Within the data limitations of voluntary participation, when wind was assumed to be the sole route of PRRSv transmission, the predictive performance of the model was fair with >0.64 AUC. Barn air filtration was associated with fewer outbreaks, particularly when exposed to high levels of viral particles. This study confirms the usefulness of the HYSPLIT model as a tool when determining seasonal effects and distances and informs the near real-time risk of windborne PRRSv transmission that can be useful in future outbreak investigations and for implementing timely control measures.

An urban air quality assessment based on a meteorological perspective.

An integrated approach to understanding all measured pollutants with multi-discipline in different time scales and understanding the mechanisms hidden under low air quality (AQ) conditions is essential for tackling potential air pollution issues. In this study, the air pollution of Sivas province was analyzed with meteorological and PM2.5 data over six years to assess the city's AQ in terms of PM2.5 pollution and analyze the effect of meteorological factors on it. It was found that the winter period (January-February-November-December) of every year except 2019-which has missing data-is the period with the highest air pollution in the province. In addition, the days exceeding the daily PM2.5 limit values in 2016, 2017, 2020, and 2021 were also seen in the spring and summer months, which inclined the study to focus on additional pollutant sources such as long-range dust transport and road vehicles. The year 2017 has the highest values and was analyzed in detail. Pollution periods with the most increased episodes in 2018 were analyzed with the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) and Dust Regional Atmospheric Model (DREAM) models. As a result of the study, the average PM2.5 values in 2017 were 31.66 ± 19.2µg/m3 and a correlation of -0.49 between temperature and PM2.5. As a result of model outputs, it was found that the inversion is intensely observed in the province, which is associated with an increase of PM2.5 during the episodes. Dust transport from northwestern Iraq and northeastern Syria is observed, especially on days with daily average PM2.5 values above 100µg/m3. Additionally, planetary boundary layer (PBL) data analysis with PM pollution revealed a significant negative correlation (r = -0.61). Air pollutants, particularly PM2.5, were found to be higher during lower PBL levels.

The Back Trajectory Study for Selected Extreme Rainfall Events Over Iraq

Abstract The Precipitations conditions having special attention in the last decade due to their impact on human health, ecosystems and other aspects such as agriculture, and hydrology. The ECMWF ERA-Interim 15-hourly (00 and 15 UTC) total precipitations in a 1° x 1° grid covering Iraq, from 29° N to 38° N and from 39° W to 48° E, with a total of 10 by 10 cells, was used. At each grid point, extremes were defined as those events in which total precipitations were above the 90th percentile for the 25 years period 1994-2019. The aim of this work is to study the precipitation cumulative extreme events in Iraq and studying the back trajectory pathway from the origin to rejoin by using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model. 60% of the precipitation cumulative extreme events occurred during the second decade. In all regions, there are important peaks in the cumulative precipitation events in 2006,2013,2014, 2016 and 2018 representing 40% of the cumulative perceptions events. The back-trajectories of rainfall events are associated with the advection of air masses from the Red Sea and the Arabian Gulf is the most influential on the amount of rain and the percentage of saturation of air masses that reach Iraq. The Mediterranean cyclone is the main source of rainfall in Iraq.

Formation of an unprecedented yellow snow episode in Xinjiang on December 1, 2018

On 1 December 2018, a heavy yellow snow fell in Urumqi (87°37′E, 43°47′N) — the largest city of northwest China's Xinjiang province, which was the first case that the yellow snow has been observed in winter. The air parcel trajectories obtained from Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model and the dust surface mass concentration from Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2) were adopted to identify the potential sources and transport paths of pollutants responsible for this yellow snow episode. The meteorological situation and the European Center for Medium-Range Weather Forecasts (ECMWF) forecast products have been utilized to analyze the supportive meteorological conditions. The results showed that the heavy snow in Urumqi was contaminated by the yellow dust originated in Karamay of Xinjiang province. The strong surface winds in Karamay lifted large amounts of dust into the atmosphere. Then the airborne dusts were transported to Urumqi rapidly by strong low-level winds, where precipitation in connection with the upper trough and the cold front lead to the yellow snow episode. This study can provide important scientific significance for predicting this kind of event (yellow snow).

The pollution of bioaerosols in hospital sewage purification process: Composition characteristics, seasonal variation and risk assessment

The hospital sewage contains many pathogenic bacteria, which may produce bioaerosol and spread diseases during the sewage treatment process. In this study, the characteristics, seasonal variation, and risk assessment of the bioaerosol in hospital sewage treatment stations (HSTS) were analyzed. The concentration of bacteria in bioaerosol generated from BRT (251 CFU/m3) was higher than that in the air at SG (75 CFU/m3). Summer (506 CFU/m3) and autumn (32 CFU/m3) showed the highest levels of bacterial and intestinal bacterial concentration in the air at BRT, respectively. The percentage of microorganisms attached to small particles (<2.1 μm) (SP) ranged from 22.64% to 66.66%, which was most prevalent in winter (66.66%). Brevundimonas (38.99%), Bacteroides (11.25%), Pseudomonas (49.48%), and Ochrobactrum (41.02%) were the dominant genera in spring, summer, autumn, and winter, respectively. The dominant bacterial genera of bioaerosol in BRT and SG showed obvious seasonal variations, Bacillus were present in each season. The hazard quotient (HQ) of males was higher than that of females (HQmales: 2.60 × 10−3, HQfemales: 2.18 × 10−3), and the inhalation risks of the bacteria were highest in summer (5.98 × 10−2). Temperature and relative humidity positively influenced intestinal bacteria in bioaerosols. The potentially pathogenic were highest in spring. One hour after the sampling point, the result of the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) mode showed that the diffusion area of bioaerosol in winter (182.03 km2) was the largest. This study focused on the bioaerosol produced by hospital sewage. The results effectively revealed the seasonal variation and risk characteristics of bioaerosols, which could serve as a theoretical foundation for the future control and reduction of bioaerosols in the process of purifying municipal water.

Simulating spatio-temporal dynamics of surface PM2.5 emitted from Alaskan wildfires

Wildfire is a major disturbance agent in Arctic boreal and tundra ecosystems that emits large quantities of atmospheric pollutants, including PM2.5. Under the substantial Arctic warming which is two to three times of global average, wildfire regimes in the high northern latitude regions are expected to intensify. This imposes a considerable threat to the health of the people residing in the Arctic regions. Alaska, as the northernmost state of the US, has a sizable rural population whose access to healthcare is greatly limited by a lack of transportation and telecommunication infrastructure and low accessibility. Unfortunately, there are only a few air quality monitoring stations across the state of Alaska, and the air quality of most remote Alaskan communities is not being systematically monitored, which hinders our understanding of the relationship between wildfire emissions and human health within these communities. Models simulating the dispersion of pollutants emitted by wildfires can be extremely valuable for providing spatially comprehensive air quality estimates in areas such as Alaska where the monitoring station network is sparse. In this study, we established a methodological framework that is based on an integration of the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model, the Wildland Fire Emissions Inventory System (WFEIS), and the Arctic-Boreal Vulnerability Experiment (ABoVE) Wildfire Date of Burning (WDoB) dataset, an Arctic-oriented fire product. Through our framework, daily gridded surface-level PM2.5 concentrations for the entire state of Alaska between 2001 and 2015 for which wildfires are responsible can be estimated. This product reveals the spatio-temporal patterns of the impacts of wildfires on the regional air quality in Alaska, which, in turn, offers a direct line of evidence indicating that wildfire is the dominant driver of PM2.5 concentrations over Alaska during the fire season. Additionally, it provides critical data inputs for research on understanding how wildfires affect human health which creates the basis for the development of effective and efficient mitigation efforts.

Preliminary discussion about the air pollution status in Afghanistan from Aerosol Optical Depth

Kabul city, the capital of Afghanistan, has suffered from poor air quality for the last two decades. This study focuses on Kabul’s spatiotemporal characteristics of aerosol optical depth (AOD). The annual and long-term 20 years of AOD data were retrieved from Moderate Resolution Imaging Spectroradiometer (MODIS) a combination of Terra and Aqua satellites using the Google Earth Engine geospatial analysis platform to determine seasonal variation and long-term trend of the aerosols. The air mass backward trajectories were calculated using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model. Furthermore, ArcGIS 10.7.1 is used to visualize the AOD over the city. The results show high AOD in summer. However, autumn is recorded as the lowest average AOD season. In addition, the 20 years of data from 2001 to 2021 shows a slight increase in AOD. We suggest that the increase in AOD in the city is due to the rapid growth of the population. The cross-boundary atmospheric aerosols affect the air quality in the city.

Radiotoxic fission products and radiological effects in the Mediterranean Sea biota from a hypothetical accident in Akkuyu Nuclear Power Plant

A combined hydrodynamic/radiobiological model was used to calculate the distribution and dose rate of significant radionuclides in the Mediterranean Sea marine organisms in the event of an accident at the Akkuyu Nuclear Power Plant (ANPP). The Hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) model was applied to simulate the dispersion and deposition of artificial radionuclide concentrations. Environmental Risk from Ionising Contaminants: Assessment and Management (ERICA) tools were utilized to assess issues related to sea health and potential hazards in case of an accident. The scenario source term profile was derived from the Fukushima nuclear power accident. Volumetric concentrations and deposition levels of pollutants increased from 1 MBqm−3 to 1 × 103 MBqm−3 and from 1 × 10−1 MBqm−2 to 1 × 10−7 MBqm−2 after 48 h. In terms of dose rates to the various marine biotas, polychaete worms had the greatest overall dose rate and the highest contribution to total dose rates attributable to 137Cs radionuclide.

Determining sources of reactive mercury compounds in Reno, Nevada, United States

There is much uncertainty regarding the sources of reactive mercury (RM) compounds and atmospheric chemistry driving their formation. This work focused on assessing the chemistry and potential sources of reactive mercury measured in Reno, Nevada, United States, using 1 year of data collected using Reactive Mercury Active System. In addition, ancillary meteorology and criteria air pollutant data, Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) analyses, and a generalized linear model were applied to better understand reactive mercury observations. During the year of sampling, a fire event impacted the sampling site, and gaseous elemental Hg and particulate-bound mercury concentrations increased, as did HgII-S compounds. Data collected on a peak above Reno showed that reactive mercury concentrations were higher at higher elevation, and compounds found in Reno were the same as those measured on the peak. HYSPLIT results demonstrated RM compounds were generated inside and outside of the basin housing Reno. Compounds were sourced from San Francisco, Sacramento, and Reno in the fall and winter, and from long-range transport and the marine boundary layer during the spring and summer. The generalized linear model produced correlations that could be explained; however, when applying the model to similar data collected at two other locations, the Reno model did not predict the observations, suggesting that sampling location chemistry and concentration cannot be generalized.

Typhoon-related changes in moisture pathways and sources for precipitation in Eastern China during the three major rainy seasons

Typhoons (TYs) and their interactions with large-scale ambient flow might exert an important control on moisture transport and precipitation. In this study, we used a Lagrangian model, the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT), to quantify changes in moisture pathways and sources associated with TY activity in relation to precipitation in eastern China during the three major rainy seasons. We compared TY-active and TY-inactive periods, i.e., the periods with and without TYs (maximum sustained surface wind speeds of up to 32.7 m/s), over the western North Pacific (WNP) to investigate TY-associated changes for the period 1979–2020. During the pre-summer rainy season in South China, anomalous moisture exhibited a north–south dipole pattern, with moisture contributions from the region extending from the South China Sea to the adjacent tropical WNP showing a 17.0% decrease during TY-active compared with TY-inactive periods (34.7% versus 41.8%, respectively), and contributions from the region extending from southwestern China to the northwestern North Pacific showing a 18.9% increase (17.0% versus 14.3%, respectively) and those from the Bay of Bengal showing a 16.6% increase (17.6% versus 15.1%, respectively). The moisture anomaly during the Meiyu rainy season in the Yangtze–Huaihe River Basin had an east–west dipole structure, with the moisture contribution from the Bay of Bengal reduced by 21.8% as compared with TY-inactive periods, and that from the WNP increased by 62.3%. During the rainy season of North China, the moisture contribution from the Indian Ocean decreased by 7.2%, while that from the WNP increased by 8.7% and from Northeast China increased by 10.0%. In addition, during TY-active periods, moisture contributions with local origins showed a slight increase.

Anthropogenic fine aerosol and black carbon distribution over urban environment

Real-time black carbon (BC) and fine particulate matter (PM) were collected from January to December 2017 with a portable Aethalometer and air sampler in the urban environment of Jamshedpur, India. In the present study, the diurnal and seasonal variations of BC and meteorological variables were analysed. The diurnal variation of BC ranged from 2.1 to 15.5 gm-3, with the lowest concentration during the monsoon season and the highest during the winter season, because in monsoon most of the BC and PM are settled down. The annual mean BC mass concentration was observed at 6.22 ± 3.95 µgm−3. While, fine PM2.5 varied from 41.6 to 260.3 µgm-3, with an annual mean of 97.49 ± 63.52 µgm−3. During monsoon, the BC mass concentration shows a value of ˂ 3 µgm−3. Additionally, the percentage contribution of BC in PM2.5 was determined to be around 5.06% (winter), 6.32% (summer), 5.20% (monsoon), and 7.21% (post-monsoon). The change in BC concerning different meteorological parameters was systematically studied, in which an exciting inverse relationship was noticed between BC concentration and temperature. The correlation between BC and wind speed was also established as a negative connection during study periods. It also observed a negative correlation with precipitation. Finally, the air back trajectory was analysed using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT), which revealed that the essential campaign of aerosol-bounded air parcels was mostly coming from the western part of India, with some marine air masses also joining from the Bay of Bengal during summer and post-monsoon. Overall study shows that highest BC and PM2.5 was observed in the winter season because of mixed layer height (MLH).

An investigation of the effects of sand and dust storms in the North East Sahara Desert on Turkish airports and PM10 values: 7 and 8 April, 2013 events.

Between April 7 and April 10, 2013, a cyclone with a value of 995hPa that developed in the central Mediterranean transported dust from the Sahara Desert towards Turkey. At 13 airports in Turkey, dust haze and widespread dust were seen during different occasions in this period and caused the observation of so-called "Blowing dust events." This cyclone blew dust towards the Cappadocia airport, and the prevailing visibility decreased to 3800m, making it the lowest value measured during the transition of this cyclone. In this study, Aviation Routine Weather Report (Metar) and Aviation Selected Special Weather Report (Speci) observations of airports in North Africa and Turkey were evaluated for the period between April 3 and April 11, 2013. With this cyclone the prevailing visibility at Benina Airport in Libya decreased to 50m on April 6, 2013. This study aims to evaluate long-distance dust transport's effects on meteorological visibility at airports in Turkey and examine the episodic changes of PM10 values measured by air quality monitoring stations. Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model outputs were used to determine the trajectories of long-distance dust particles. Powder red, green, and blue (RGB) and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images, Cloud-Aerosol LIDAR Infrared Pathfinder Satellite Observations (CALIPSO) images, the Barcelona Supercomputing Center-Dust Regional Atmosphere Model (BSC-DREAM8b) outputs, and Global Forecast System (GFS) synoptic maps were used for analysis. In addition, PM10 values obtained from air quality monitoring stations were examined. According to the data obtained from the CALIPSO images, the dust concentration on the Eastern Mediterranean reaches up to 5km. The episodic values obtained from certain air quality measurement stations are Adana 701, Gaziantep 629, Karaman 900, Nevşehir 1343, and Yozgat 782µg/m3 on an hourly average.

Isotopic insights on quantitative assessments of interaction of eco-hydrological processes in multi-scale karst watersheds

The dynamics of hydrological processes and the storage mechanisms of karst water resources are the most important issues in karst hydrology. The impact of environmental changes on water quantity, and the evaluation and quantification of eco-hydrological processes remain poorly addressed. In this study, high-frequency continuous monitoring in multi-scale karst watersheds in Southwest China combined the approaches of water isotopes and the hybrid single-particle lagrangian integrated trajectory (HYSPLIT) model to identify the recharge mechanisms between atmospheric vapor, rainfall, surface water, and groundwater, and to reveal the interaction of eco-hydrological processes. The dominant moisture sources in Puding (PD) County were the Indian Ocean (43–69%) and local moisture (24–33%). The δ18O and deuterium excess (d-excess) values showed a positive correlation indicating that secondary or sub-cloud evaporation was prominent in the wet seasons. Karst water line-conditioned excess (lc-excess) indicated that karst water interacted with recent precipitation, groundwater, and evaporation across seasons. Owing to its specific hydrogeological structure, surface water and rainwater have a higher contribution rate to groundwater replenishment. The Chenqi stream replenished the Houzhai River mainly in the form of groundwater, with percentages ranging from 38.1 to 93.5% in the wet season, and 47.8–80.1% in the dry season. In the Houzhai outlet, surface water and groundwater interconverted frequently with a percentage of 45.6–49.1%. We believe this is the first systematic study to quantify the supply relationship between water vapor transport, rainfall, surface water and groundwater in the Chinese karst zone, making a significant move forward in the field of karst hydrological processes and improving the efficiency of water resource evaluation and management.

Bangkok port and coastal regions of Thailand under atmospheric PM2.5 pollution: A hypothetical nuclear power plant accident

The atmospheric transport pathways of 1) a hypothetical accident of aerosol at Ninh Thuan Nuclear Power Plant (NPP) in Vietnam to the coastal area of Thailand and 2) the source of PM2.5 near the area of Bangkok port were selected for case studies. The overviews of atmospheric transport patterns were analyzed using the long-term mean wind and the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model for simulating the transport pathway. The results found that the possible pathway of aerosol from Ninh Thuan NPP to coastal regions of Thailand was present over the entire year, except for the southwest monsoon month. The percentage of aerosol transport reaching the coast of Thailand is highest in the southern coastal part, found in January-April and October-December, with a maximum in January (approximately 84 %). For PM2.5 pollutants on a poor day, strongly positive and negative significant relationships with relative humidity (RH) (r = 0.44, rs = 0.46) and wind speed (r = -0.46, rs = -0.47) were found, respectively. The concentration of PM2.5 is slightly negatively correlated with wind direction. In addition, the upper air transport may bring PM2.5 pollutants from neighboring provinces or Southeast Asia to Bangkok port, Thailand, which appeared in anticyclone form in the upper atmosphere before the day which exceeded the safety level of PM2.5. Highlights The possible pathway of aerosol from Ninh Thuan NPP to coastal regions of Thailand is present over the entire year The percentage of aerosol transport from Ninh Thuan NPP reaching the coast of Thailand is highest in the southern coastal part during the northeast monsoon The exceeding air pollution problem in Bangkok comes from unfavorable meteorological conditions The anticyclone center in the upper atmosphere of Vietnam, Laos, and Cambodia formed before the day, which exceeded the safety level at a poor level

Potential Source Area and Transport Route of Atmospheric Particulates in Xi’an, China

Atmospheric particulate pollution is one of the most common pollution related issues and poses a serious threat to human health. PM2.5 and PM10 are important indicators of atmospheric particulate pollution currently. Based on the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model, the hourly 72 h backward trajectory of particulate matter in Xi’an from March 2019 to February 2022 was calculated, and the main path of air flow to Xi’an was studied by cluster analysis. Combined with hourly concentration monitoring data of PM2.5 and PM10 at each station, the potential source area of particles in Xi’an was calculated by potential source contribution factor analysis and concentration weighted trajectory analysis. The results show that Xi’an was most polluted in winter, followed by autumn and spring, and cleanest in the summer. The annual average mass concentrations of PM2.5 and PM10 are 48.5 ± 28.7 μg/m3 and 89.2 ± 39.2 μg/m3, respectively, both exceeding the national secondary standard for ambient air quality. On an annual basis, back-trajectory analysis showed that predominantly transport was rapid from the northwest (44%). Transport from the other sectors were 24%, 19%, and 14% from the northeast, southeast, and southwest, respectively, and featured lower windspeeds on average. The potential source areas of particulate matter in Xi’an in the spring are mainly located at the junction of Chongqing, Hunan, and Hubei, and parts of the southeast and north of Sichuan. This study provides context for air quality and atmospheric transport conditions in this region of China.

Investigation of the aerosol's optical properties over the dust prevalent semi-arid region at Jaipur, northwestern India

Jaipur is a key site to study Aerosol Optical Depth (AOD) due to unpredictable variation of atmospheric Aerosols particles in the recent years. Aerosols Optical Depth (AOD) and Angstrom Exponent (AE) were investigated over the semi-arid region, Jaipur (26.9124° N, 75.7873° E) Northwestern India during the April-2009 to December-2017 utilizing AERONET CIMEL sun-photometer level-2 data for aerosols spatial and temporal characteristics. The Aerosols Optical Depth AOD&lt;sub&gt;500&lt;/sub&gt; nm, AOD&lt;sub&gt;440&lt;/sub&gt; nm, and AOD&lt;sub&gt;340&lt;/sub&gt; nm were observed maximum in July-2011 and minimum observed in March-2013. The monthly mean Angstrom Exponent (AE) was maximum (1.53 ± 0.43) for 500-870 nm, (1.49 ± 0.4) for 440-870 nm, and (1.19 ± 0.32) for 340-440 nm in September 2011, while the lowest value recorded (0.26 ± 0.12) for 340-440 nm, (0.16 ± 0.08) for 440-870 nm and (0.15 ± 0.07) for 500-870 nm during June 2014. The backward trajectories analysis was performed using the Hybrid Single-Particle Lagrangian Integrated Trajectories (HYSPLIT) model to determine the sources of large aerosol loadings over the Jaipur region, and air mass simulations from the back trajectory, suggest that the Thar desert northwestern part of India is the source of highest aerosols dust particle over the study area during the pre-monsoon season.

Classifying aerosol type using in situ and satellite observations over a semi-arid station, Anantapur, from southern peninsular India

The chemical composition of aerosols is crucial for understanding aerosol radiative characteristics, classifying aerosol sources, and improving atmospheric aerosol satellite retrieval algorithms. In this study, the absorption angström exponent (AAE) and scattering angström exponent (SAE) values are derived from an aethalometer and three wavelength integrating nephelometer. MERRA-2 (Modern-Era Retrospective Study for Research and Applications) speciated aerosol optical depths and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) aerosol subtypes are utilized for the identification of a possible aerosol type over a semi-arid station, Anantapur, in Southern peninsular India from March 2016 to February 2018. Analysis from MERRA-2 for different aerosols illustrates that sulphate aerosol optical depth (SO4 AOD) dominates (>0.16) during winter and post-monsoon, while dust aerosol optical depth (DU AOD), organic carbon optical depth (OCAOD), and sulphate aerosol optical depth (SO4 AOD) mainly contributed (>0.07) to the total AOD during summer. SO4 AOD accounted for over ∼ 50% of total AOD during winter and post-monsoon, whereas DU AOD accounted for<10% of total aerosol optical depth in the respective seasons. The seasonal mean values of SAE (AAE) were found to be 1.95 ± 0.37 (1.03 ± 0.08), 1.65 ± 0.55 (1.03 ± 0.08), 2.53 ± 0.20 (1.03 ± 0.08) and 2.66 ± 0.15 (1.03 ± 0.08) during summer, monsoon, post-monsoon, and winter, respectively. AAE and SAE classification schemes showed that during winter, summer, and post-monsoon, continental polluted aerosols dominated (>56%), while dust and marine pollution aerosols dominated (∼47%) to the total aerosols during the monsoon. Continental aerosols are predominant during all seasons except for monsoon, and result from local anthropogenic activities and the transport of mineral dust particles and smoke aerosols from the north and western India. The observed aerosol types are consistent with the air mass back-trajectory cluster analysis performed with the help of Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT). The various aerosol types (absorbing and non-absorbing) and their change over a region are essential for for reducing the uncertainty in the assessment of aerosol radiative effects.

Air Pollution Prediction Based on Changes in Monsoon Wind Direction by Using Trajectory-Geospatial Approach

Industrial areas are typically associated with hazardous levels of air pollution to human health and the environment. The growing number of factories in the area poses an ever-greater threat to the surrounding communities. One of the several incidents pointing to poor air quality in the industrial region is the severe air pollution incident that occurred in Pasir Gudang in June 2019 which brought adverse health impacts to nearby schoolchildren. This study intended to ascertain the role of meteorological factors and impacts on the dispersion of air pollution in the Pasir Gudang Industrial Area at the time of the occurrence. The air pollution distribution patterns were predicted using a trajectory-geospatial method, the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model and Geographic Information System to account for the impact of the monsoon by determining which areas might be hit the worst as the shifted of monsoon seasons. Based on the results, the forward-air mass trajectories showed direct influence by the wind changes in monsoon seasons (Southwest Monsoon, Northeast Monsoon, and Inter-Monsoon). The geospatial maps showed the potential areas affected by the air pollution incident were highly distributed within 1 km, associated with lower dispersion of air pollution at 1000m above ground level height. The findings can serve as a guideline for local authorities in decision-making to develop better standard operating procedure in managing future air pollution threats and to improve industrial location planning in reducing air pollution impacts on the surrounding area.