Local Meteorological Parameters Research Articles

Comparison of energy consumption prediction models for air conditioning at different time scales for large public buildings

Large public buildings have complex functions and high air conditioning energy consumption, and the prediction of energy consumption for air conditioning in different time scales can realise different energy saving management needs. Taking a large public building in Guangzhou as a research case, the long and short-term memory neural network (LSTM) is used as the basis to establish the machine learning model for energy consumption prediction for air conditioning at different time scales in terms of 10 min, hourly and daily, and a comparative analysis is carried out. The original data were analysed and processed by Z-Score and Local Outlier Factor (LOF) outlier detection methods. The relationship between meteorological parameters for Typical Meteorological Year (TMY) and the actual local meteorological parameters and energy consumption for air conditioning was analysed using linear regression, Pearson and Spearman correlation coefficients, taking meteorological parameters as input variables. The results show that comparing energy consumption for air conditioning with the meteorological parameters in TMY and actual local meteorological parameters, the actual meteorological parameters had a stronger correlation energy consumption for air conditioning. When the actual local meteorological parameters are taken as input variables, the mean absolute percentage error (MAPE) is reduced by about 0.85 %, and the coefficient of determination (R2) is increased by about 0.01. At the same time, the MAPE and R2 of the 10-min energy consumption prediction model are about 6.43 % and 0.9738, respectively, the MAPE and R2 of the hourly prediction model are about 9.29 % and 0.9568, respectively, while the MAPE and R2 of the daily prediction model are about 25.76 % and 0.7998. Meanwhile, an improved energy consumption prediction model is proposed to reduce the daily energy consumption prediction error to 6.36 % for MAPE, and the R2 of this model is 0.9927.

Urban microclimate prediction based on weather station data and artificial neural network

Urban microclimate has a significant impact on building energy consumption. Building energy modeling (BEM) requires accurate local weather conditions near a target building, whereas Typical Meteorological Year (TMY) weather inputs often use remote airport weather data. An artificial neural network (ANN) model is presented in this study to predict urban microclimates based on long-term measurements from local weather stations near urban buildings and their significance in analyzing building energy consumption. By utilizing only a few months of data, the ANN model could connect local and remote meteorological parameters for a whole year. The 20-year historical weather data at the airport was then used to generate a local TMY. Based on the original and local TMYs, this study compared building heating and cooling loads. This method was evaluated for five weather stations within the city of Montreal to assess the impact of the local microclimate on the energy consumption of buildings. Based on locations, urban microclimate contributed to an additional 2 % to 14 % cooling energy consumption and a reduction of 1 % to 10 % winter heating energy consumption.

Assessment of Frost Depth and Frost Heave in Romania

The investigation of extreme weather and climate events has gained momentum in the last few decades, spurred in part by political involvement. However, not enough attention has been paid to the historical recorded data of climate monitoring. An accurate estimate of maximum soil frost depth is an important factor in determining construction costs and structures’ foundations, and designers also need reliable information about local meteorological parameters. In this study, the calculations of the parameters for the city of Arad are presented as an example. An updated Romanian frost depth map and a new Romanian freeze–thaw cycle (FTC) map are needed since recent climate studies have documented an increase in global and regional surface temperatures, with the greatest shift in warming occurring over the last three decades. This trend is particularly pronounced when comparing the 1990–2020 period with earlier periods, which could be indicative of broader climate change trends. It is important to note that these conclusions are based solely on the provided statistical parameters and do not take into account other potential factors influencing temperature trends in the region. The results from this investigation could be used to achieve a reduction in frost disasters and the formulation of policies and measures for the adaptation of geotechnical/structural design for Romanian territory. They may also support the development of maps that help to visualize and understand the evolving climate patterns in the region, including changes in frost depth and the frequency of freeze–thaw cycles, which are important for various sectors such as agriculture, construction, and infrastructural planning. Given the documented increase in global and regional surface temperatures, updating such maps will also provide valuable information for policymakers, researchers, and stakeholders on how to adapt to ongoing climate change and its impacts on the Romanian region.

Radiative parameters specific to Braşov urban area

The paper objective is to present the input data needed for the renewable systems design, namely the recording and the analysis of the local meteorological and climatological parameters such as the solar radiation, the wind speed, the clearness index and the atmospheric turbidity factor. The diagrams presented are obtained on the basis of the meteorological data recorded during three years, 2006-2008. The used meteorological data, for this study, were recorded with a local weather station positioned on the building roof of the Transilvania University of Braşov. The paper ends with an analysis of some very important diagrams concerning the solar and wind power for Braşov area, and there are worded conclusions derived from the comparative analysis of these values.

Modeling the Normalized Urban Heat Island for the City of Karlsruhe by Linking Urban Morphology and Green Infrastructure

Citizens in urban areas are affected by the urban heat island (UHI) effect, resulting in increased thermal heat compared to rural areas. This threat is exacerbated by global climate change. Therefore, it is necessary to assess human thermal comfort and risk for decision making. This is important for planners (climate resilience), the health sector (information for vulnerable people), tourism, urban designers (aesthetics), and building architects. Urban structures modify local meteorological parameters and thus human thermal comfort at the microscale. Knowledge of the pattern of a city’s UHI is typically limited. Based on previous research, generalized additive models (GAMs) were built to predict the spatial pattern of the UHI in the city of Karlsruhe. The models were trained with administrative, remotely sensed, and land use and land cover geodata, and validated with measurements in Freiburg. This identified the hot and cold spots and the need for further urban planning in the city. The model had some limitations regarding water bodies and anthropogenic heat production, but it was well suited for applications in mid-latitude cities which are not topographically characterized. The model can potentially be used for other cities (e.g., in heat health action plans) as the training data are freely available.

Some Features of Black Carbon Aerosols Connected with Regional Climate Over Pristine Environment

The authors report the results of aethalometer black carbon (BC) aerosol measurements carried out over a rural (pristine) site, Panchgaon, Haryana State, India during the winter months of 2021–2022 and 2022–2023. They are compared with collocated and concurrent observations from the Air Quality Monitoring Station (AQMS), which provides synchronous air pollution and surface meteorological parameters. Secular variations in BC mass concentration are studied and explained with variations in local meteorological parameters. The biomass burning fire count retrievals from NASA-NOAA VIIRS satellite, and backward airmass trajectories from NOAA-ERL HYSPLIT Model analysis have also been utilized to explain the findings. They reveal that the north-west Indian region contributes maximum to the BC mass concentration over the study site during the study period. Moreover, the observed BC mass concentrations corroborate the synchronous fire count, primary and secondary pollutant concentrations. The results were found to aid the development of mitigation methods to achieve a sustainable climate system.

Atmospheric black carbon in a key location of the Chilean Central Andes: Identifying patterns, sources, and potential impacts

Black carbon (BC) has been identified as one of the primary contributors to climate change, primarily due to its exceptional capacity to absorb solar radiation. This leads to a reduction in the albedo of ice and snow surfaces, consequently accelerating their melting. In this work, measurements of atmospheric BC are presented, carried out from October 2016 to December 2019 in Portillo, Chilean Central Andes, in the NUNATAK-1 laboratory refuge (3000 m.a.s.l). This site is close to the Chile-Argentina border and is situated along a heavily trafficked road connecting the two countries. Owing to weather conditions, this route operates on a seasonal schedule, remaining open 24 h during warmer months, while in winter, traffic is restricted to the hours between 8 a.m. and 8 p.m. Access to vehicles is prohibited during periods of heavy snowfall. BC concentrations were continuously monitored by a Multi-Angle Absorption Photometer (MAAP). The variability and temporal cycles of BC levels were investigated in relation to local vehicular activity and meteorological parameters, through multiple linear regression analysis. Results show that vehicular traffic variations led to a systematic pattern in the atmospheric BC measured, with a preponderant role of meteorological factors. During the summer months, BC levels reached their peak (mean of 0.77 μg m−3), with maximum values recorded during the nighttime and early morning hours. In line with this finding, summer counted the highest number of vehicles circulating in the area. In contrast, the winter months exhibited lower average BC atmospheric concentrations compared to summer months (0.35 μg m−3), characterized by a distinct hourly profile. In a complementary way, forward trajectories using the HYSPLIT model were performed for various meteorological conditions. The findings indicated that emissions from the study area had the potential to extend to neighboring glaciers. This impact on the basins' hydrology, vital and fragile components of the Andean region, magnifies climate change effects in these cryospheric zones. Notably, the latest IPCC report highlights limited research on aerosol effects on mountain snow and glaciers. This study also presents the first estimates of high-altitude BC source assessments in Chile and South America. These insights could enhance global atmospheric models in mountainous regions, enhancing their accuracy and utility.

Characterization of urban aerosol pollution before and during the COVID-19 crisis in a central-eastern European urban environment

The worldwide restrictions due to the COVID-19 pandemic induced a radical change in urban air quality. The objective of this work was to study and understand the variation of PMcoarse and PM2.5 pollution besides other air quality (AQ) parameters in the city of Debrecen, Hungary from 2018 to 2022. To achieve this goal, we introduced an integrated approach that combines source apportionment by receptor modelling, trajectory-based statistical methods and takes into account local and regional meteorology. Concentration, elemental composition, and sources of atmospheric particulate matter (APM) were determined for four lockdown periods with varying levels of restrictions, two transition intervals and two relaxation periods in 2020-22, and they were compared to corresponding baseline values from 2018-19. The concentration, composition and sources of PM2.5, PMcoarse showed strong seasonality. The main component of PMcoarse was mineral dust (32%), while BC (15%), sulphate aerosols (19%) and mineral dust (13%) were the major constituents of PM2.5. Source apportionment by Positive Matrix Factorization identified 8 sources on both size fractions: two types of soil, traffic, combustions, biomass burning, biogenic emission, sea salt, construction, roadworks, secondary sulphate. The relative contribution of sources did not change during the pandemic compared to the previous two years. However, considering the whole 2-year-long period affected by restrictions, a 20–25% reduction was detected in the concentrations and source contributions, apart from temporary sources like roadworks or sea salt episodes. The changed habits of the population could be traced in the evolution of APM. Before the pandemic, increased concentration of traffic related pollutants was characteristic of the school year, while in 2020-21 it was more typical in the summers, the relaxation periods. On the other hand, staying at home during lockdowns led to a significant increase in biomass burning from domestic heating in spring 2020 and 2021. By studying the individual lockdown and relaxation periods, we have shown that the evolution of APM pollution in relatively short, 2-3-month intervals was significantly influenced by local and regional meteorological parameters, the origin of air masses, desert dust episodes and periodic construction works.

Trace elements and microbial community composition associated with airborne PM2.5 in wetlands: A case study in Tablas de Daimiel National Park

Tablas de Daimiel National Park (TDNP) is one of the most important wetlands in the Iberian Peninsula. Due to its location near various cities and new industries focused on agricultural waste revalorization, we investigated concurrently the concentrations of particulate matter 2.5 (PM2.5) mass, trace element composition, and associated microbial communities (bacteria and fungi) during a year-long study. The goal of this study was to explore the dependencies among these physicochemical and microbiological parameters on a seasonal time scale. Additionally, we assessed meteorological conditions and back trajectories to shed light on atmospheric mechanisms and sources related to these elements. We found the variability of PM2.5 to be influenced by local meteorological parameters. Through the analysis of crustal enrichment factors (EFs), bivariate correlations, and air mass patterns, we determined that soil resuspension was the primary contributor to elevated metal concentrations in PM2.5 within the park, followed by other minor sources, such as traffic emissions and Sahara dust intrusions. The measured metal levels were used to calculate the ecological risk in the area, resulting in a low ecological risk index (RI) of 52. Shifts in microbial community structure were observed to be mainly driven by changes in air temperature and Cu concentration. The results from this study contribute to a better understanding of the environmental dynamics in TDNP. Taken together, our findings will aid in the development of effective strategies for its conservation and management.

A Radial Basis Function Neural Network Algorithm for the Simultaneous Retrieval of Two Meteorological Parameters from Solar Radiation

Local meteorological parameters are key in understanding the frequency of occurrence of extreme weather conditions such as floods, and droughts, among others. In this study, we present a method for simultaneous retrieval of two weather parameters. The method is based on already measured monthly average values of weather parameters from 2011 to 2016, which were used to train a Feed-forward radial basis function neural network (RBFNN) to obtain a fast and accurate method to compute global solar radiation for specified weather parameters pair. In inverse modelling, a multidimensional unconstrained non-linear optimization was employed to retrieve the weather parameters pair. The new approach was validated using weather parameter data measured at the Department of Physics, Makerere University (0.31° N, 32.58° E, 1200 m). Statistical tools were used to evaluate the method's performance. In the Feed-forward artificial neural network (ANN), the correlation coefficient (R), mean bias error (MnB), root mean square error (RMSE), and mean percentage error (MAPE) were in the ranges 0.80-0.95, -0.0011-0.0077, 0.55-1.04 and 2.49%-5.82%, respectively. The pairs (sunshine hours, relative humidity) and (sunshine hours, minimum temperature) had the highest correlation coefficient of 0.95. In the inverse artificial neural network (ANNi), the R, MnB, RMSE and MAPE were in the ranges 0.3-0.9, 0.01-0.08, 0.51-11.14 and 2.1%-14.5%, respectively. The pair (sunshine hours, relative humidity) had the highest correlation coefficients of 0.92 and 0.62, respectively. The method helps in obtaining weather parameter data sets in places where measuring equipment is lacking or during days when measuring equipment malfunctions.

Compositions of hydrogen and oxygen isotopes of precipitation in Xiamen, southeast China coast: Seasonal variations, synoptic processes, and typhoons impact

AbstractIn this study, the δ2H and δ18O values of 162 precipitation samples (including 33 typhoon‐related samples), collected in Xiamen, southeast China coast, during June 2018 to August 2019, were investigated and analysed. The findings indicate an obvious seasonal variation in the δ2H and δ18O, primarily influenced by the East Asia Monsoon system with notable impact from typhoon occurrences during summer months. The synoptic time‐series variation of δ2H and δ18O exhibits greater complexity: three distinct types of isotopic variations were observed, indicating a clear influence from re‐evaporation processes and continuing equilibrium fractionation during condensation. The variation patterns of δ2H and δ18O were dominated by the local meteorological parameters that govern the re‐evaporation, whereas the overall isotopic composition of precipitation was primarily regulated by the moisture sources. The unique atmospheric structure and dynamic processes of typhoons led to a three‐stage variation of δ2H and δ18O in precipitation as the distance between the typhoon's centre and the study area changed. The consistent configuration of typhoon structure prompts analogous staged changes in different typhoon‐related precipitation. The trajectory of moisture source for typhoon‐related precipitation demonstrates a clear spiral structure (excluding typhoon Yutu), revealing notable impacts from typhoon events.

Ozone and PM2.5 behavior in small cities in southern Brazil

Air pollution is one of the greatest challenges facing humanity today. The number of studies has grown annually, but most of the time they have been conducted in megacities or heavily industrialized regions. In this study, we aimed to evaluate the behavior of O3 and PM2.5 in 5 small cities (< 1500 inhabitants) in southern Brazil, comparing them with reference cities in their micro-regions and with the metropolis Porto Alegre, capital of the state of Rio Grande do Sul. During the short period evaluated, the levels of pollutants in small cities would not differ from those found in the reference cities and were equivalent to the levels in the studied metropolis. Also, during 2 periods (18 to 20 September 2021 and 30 September to 2 October 2021) there were episodes of high levels of PM2.5, superior to the national air quality criteria in 4 of the 5 small cities. The likely origin of the pollutants is the long-distance transport of pollutants from biomass burning in the Amazon. Multiple regression analysis showed little or no influence of local meteorological parameters for O3 and PM2.5 levels, but showed a strong association with pollutant levels in the respective reference cities. Understanding the dynamics of air pollutants in small cities is essential to understand some health outcomes in these populations and has been recently recommended by the World Health Organization.

Influence of atmospheric circulation and local parameters on activity concentration of gross alpha and gross beta in Granada, Spain

This work analyses the influence of local meteorology and air mass origin on radioactivity levels measured in Granada (Southern Spain). To the scope, gross α and gross β activities at ground-level air were weekly recorded from 2006 to 2021, by collecting aerosol filters and successively analysing them by low-level proportional counting. Time series decomposition shows a strong correlation between the seasonal components of gross α and gross β and the local meteorological parameters, in particular temperature and wind speed. Back trajectories reaching the study area during the sampling, calculated at three height levels (750 m, 1500 m, and 3000 m a.s.l.), were analysed by three methods with the aim to determine the main characteristics of air masses reaching Granada, and their impact on airborne radioactivity levels. The results of the residence time and clustering analyses showed that an increase in the radioactivity levels in the southeast of Spain is connected with air masses coming from the Mediterranean at 750 m and from the South (Sahara Desert) at 3000 m. In line with the outcomes obtained by the cluster analysis, the Concentration Weighted Trajectory (WCWT) results highlight that the air masses transported from Mediterranean and African regions are a potential source of gross α and gross β. Moreover, the findings of the WCWT bring a new aspect by revealing that the southern France region can also have a significant contribution to gross alpha and beta activities.

Anomalous stable hydrogen-oxygen isotope characteristics and water vapor sources of autumn precipitation in the Weihe River basin, Northwest China.

The extreme changes in autumn rain have significant impacts on the ecological environment of Weihe River basin. Based on 117 autumn rain samples and corresponding meteorological data from 2015 to 2021 at Yangling located in the middle of Weihe River basin, we investigated the stable hydrogen and oxygen isotope composition and water vapor sources of precipitation. The results showed that, (1) extreme changes in autumn rainfall in the study area occurred frequently in recent years, which could be divided into extreme-high autumn precipitation year (HAP, 2021), general autumn precipitation year (GAP, 2015-2017, 2019-2020) and extreme-low autumn precipitation year (LAP, 2018) based on the autumn rain index (ARI); (2) the stable isotopes of different types of precipitation differed significantly, with a pattern of LAP>GAP>HAP for both δ2H and δ18O values. the variations of d-excess values and the slopes and intercepts of the meteoric water lines of autumn rain showed opposite trends. The main factor controlling autumn rain anomaly was not the local meteorological parameters, but the El Nino-Southern Oscillation and the Indian Ocean dipole events, which could explain 99% and 93% of the autumn rain isotopic variations, respectively. These coupling phenomena affected water vapor transport intensity of the marine air mass to the northwest inland, which determined autumn rainfall amount and the stable hydrogen-oxygen isotope composition. Our results would be helpful for improving the understanding of autumn rain anomalies in West China, and provide basic data and theoretical support for regional hydrological model building, would thereby better serve water resources management and disaster prevention and reduction.

Intra- and inter-city variability of PM2.5 concentrations in Greece as determined with a low-cost sensor network

Measurements of PM2.5 concentrations in five major Greek cities over a two-year period using calibrated low-cost sensor-based particulate matter (PM) monitors (Purple Air PA-II) were combined with local meteorological parameters, synoptic patterns and air mass residence time models to investigate the factors controlling PM2.5 spatiotemporal variability over continental Greece. Fourteen sensors nodes in Athens, Patras, Ioannina, Xanthi, and Thermi (in the Metropolitan Area of Thessaloniki) were selected out of more than 100 of a countrywide network for detailed analysis. The cities have populations ranging from 65k to 3M inhabitants and cover different latitudes along the South-North axis. High correlations between the daily average PM2.5 levels were observed among all sites, indicating strong intra- and inter-city covariance of concentrations, both in cold and warm periods. Higher PM2.5 concentrations in all cities during the cold period were primarily associated with low temperatures and stagnant anticyclonic conditions, favoring the entrapment of residential heating emissions from biomass burning. Anticyclonic conditions were also connected to an increased frequency of PM2.5 episodes, exceeding the updated daily guideline value (15 μg m−3) of the World Health Organization (WHO). During the warm period, nearly uniform PM2.5 levels were encountered across continental Greece, independently of their population size. This uniformity strongly suggests the importance of long-range transport and regional secondary aerosol formation for PM2.5 during this period. Peak concentrations were associated mainly with regional northern air flows over Greece and the Balkan Peninsula. The use of the measurements from dense air quality sensor networks, provided that a robust calibration protocol and continuous data quality assurance practices are followed, appears to be an efficient tool to gain insights on the levels and variability of PM2.5 concentrations, underpinning the characterization of spatial and seasonal particularities and supporting real-time public information and warning.

Air Quality Index (AQI) Did Not Improve during the COVID-19 Lockdown in Shanghai, China, in 2022, Based on Ground and TROPOMI Observations

The lockdowns from the coronavirus disease of 2019 (COVID-19) have led to a reduction in anthropogenic activities and have hence reduced primary air pollutant emissions, which were reported to have helped air quality improvements. However, air quality expressed by the air quality index (AQI) did not improve in Shanghai, China, during the COVID-19 outbreak in the spring of 2022. To better understand the reason, we investigated the variations of nitrogen dioxide (NO2), ozone (O3), PM2.5 (particular matter with an aerodynamic diameter of less than 2.5 μm), and PM10 (particular matter with an aerodynamic diameter of less than 10 μm) by using in situ and satellite measurements from 1 March to 31 June 2022 (pre-, full-, partial-, and post-lockdown periods). The results show that the benefit of the significantly decreased ground-level PM2.5, PM10, and NO2 was offset by amplified O3 pollution, therefore leading to the increased AQI. According to the backward trajectory analyses and multiple linear regression (MLR) model, the anthropogenic emissions dominated the observed changes in air pollutants during the full-lockdown period relative to previous years (2019–2021), whereas the long-range transport and local meteorological parameters (temperature, air pressure, wind speed, relative humidity, and precipitation) influenced little. We further identified the chemical mechanism that caused the increase in O3 concentration. The amplified O3 pollution during the full-lockdown period was caused by the reduction in anthropogenic nitrogen oxides (NOx) under a VOC-limited regime and high background O3 concentrations owing to seasonal variations. In addition, we found that in the downtown area, ground-level PM2.5, PM10, and NO2 more sensitively responded to the changes in lockdown measures than they did in the suburbs. These findings provide new insights into the impact of emission control restrictions on air quality and have implications for air pollution control in the future.

Temporal variations of precipitation driven by local meteorological parameters in southwest China: Insights from 9 years of continuous hydro-meteorological and isotope observations

Study regionNorthwest of Guangxi Province, southwest China. Study focusKnowing the role of isotopes in precipitation can improve understanding of climate change and eco-hydrological processes in complex critical-zone systems. However, knowledge of the influence of local meteorological factors on variations of precipitation isotopes remains limited as high-resolution rainwater sampling is lacking. New hydrological insights for the regionThe temporal characteristics (intra-annual, seasonal, and monthly scales) of precipitation were analyzed using daily precipitation isotopes data from 2011 to 2020 and event-resolving data from 2019 to 2020 at Huanjiang in southwest China. The boosted regression tree model was used to explore the relationship between local meteorological factors and δ18O values. On an intra-annual scale, δ18O, δD, and d-excess values exhibited significant sinusoidal fluctuation, being lower and higher during the wet and dry seasons respectively. Temperature is the most sensitive meteorological factor to monsoon climate response. On the seasonal scale, ET0 explains 37.5% of isotopic variations in the dry season, thus indicating that the influence of evaporation on the rainwater isotopes variation should be considered. The “amount effect” of rainwater isotopes was clearly shown on the monthly scale. However, precipitation isotopes did not follow the “amount effect” on an inter-event scale, which can be explained by large-scale convection activity. These findings can improve interpretations of isotopic variations in precipitation in humid subtropical climate zones.

Analysis of 222Rn Surface Concentrations in the Basque Country (Spain): A Case Study of Heat Waves.

The objective of this study was to characterize radon concentrations registered in the Radiological Surveillance Network of the Basque country in relation to local meteorological parameters, and to determine its behaviour under heatwave events. For this purpose, radon measurements and meteorological parameters from June 2012 to June 2015 were analysed at two sites, Bilbao and Vitoria (northern Spain), in a region characterized by complex orography, causing large temporal and spatial variability in meteorological conditions. Yearly, seasonal, and diurnal cycle differences and similarities were investigated at both sites. The temporal evolution of radon concentration was analysed at both sites during the two heatwave periods officially identified by the State Meteorological Agency (8-11 August 2012 and 17-23 August 2012). The analysis revealed two different patterns of radon concentrations, in terms of both time and intensity, under this synoptic pattern, making it also possible to identify regional transport channels of radon concentrations between the two sites. This set of results evidences the adequate position of both stations to represent the spatial and temporal evolution of radiological variables continuously in this region.

Prediction and Analysis of Nuclear Explosion Radioactive Pollutant Diffusion Model

This study presents a model for the dispersion of radioactive smoke clouds from a nuclear weapon explosion. A model based on a modified Settlement model is chosen to simulate the dispersion of radioactive contaminants from a nuclear explosion in the atmosphere. The arrival time and dose rate of radioactive fallout at various distances in the downwind direction are given for different equivalents of the surface explosion and typical meteorological conditions. Thus, the prediction of the dispersion of radioactive contaminants from a nuclear explosion can be achieved under the conditions of known nuclear explosion equivalence and local meteorological parameters. This provides a theoretical basis for the estimation of the affected environment and the input of rescue forces after the explosion.

Multisite Scenarios of Black Carbon and Biomass Burning Aerosol Characteristics in India

Black Carbon (BC) aerosols are not only substantial climate-forcing drivers but also impact human health. The spatial distribution of BC aerosols depends on the combination of anthropogenic activities and meteorological conditions. In this study, we used the India Meteorological Department (IMD) Black Carbon Observational Network datasets to assess the diurnal, seasonal, and long-term BC trends for the period, 2016–2021. The majority of the IMD’s BC monitoring stations show an overall declining trend in the BC mass concentration during the study period in India. Maximum BC concentrations are observed in the post-monsoon and winter seasons due to the stubble-burning activity and lower values of Atmospheric Boundary Layer Height (ABLH). Minimum concentrations are observed at all stations in the monsoon season due to the wet scavenging of aerosols by rain. There is a clear decrease in the BC mass concentration from winter to monsoon months and an increase in the post-monsoon months. Regional emissions from crop residue burning in the post-harvesting seasons are the main contributing factor for extremely high levels of BC mass concentration. Low wind speed and shallow mixed layer were found to be the main reasons for high levels of aerosol concentration during the winter season. There is an increasing trend in Biomass Burning (BB) at most of the stations except for Thiruvananthapuram, where a prominent decreasing trend in BC concentration is also noticed. In the present study, the impact of local meteorological parameters such as wind, temperature, rainfall and Atmospheric Boundary Layer Height on BC mass concentration is investigated. The results show a negative correlation with rainfall, relative humidity, wind speed, temperature and ABL height. Both local activity and long-range transport at each study site are also found to be responsible for the significant changes in BC mass concentration.