Atmosphere, Volume 14, Issue 2 (February 2023) – 228 articles

Phthalates can be found in personal care products as solvents and plasticizers in various polymers, especially PVC, wall coverings, certain paints, vinyl floor coverings, electronic devices, medical devices, food packages, toys, cables and other products. Humans are ingesting food products that contain phthalates, or they have dermal contact with phthalate-containing material, such as clothes, PVC gloves, personal care products or house dust. In this study, samples of dust from several houses in Kozani city, Greece, were collected and analyzed for phthalate concentration, and the potential association with building characteristics was examined utilizing detailed checklists. Samples were taken from the vacuum cleaner of the houses and extracted with ethyl acetate, and then analyzed with GC-MS in the SIM mode. The levels of phthalate ranged from 10.57 to 221.19 μg/g for Di-iso-butyl phthalate (DiBP), 4.03 to 264.91 μg/g for Di-n-butyl phthalate (DBP), 0.72 to 20.22 μg/g for benzyl-butyl phthalate (BBP) and 62.73 to 1233.54 μg/g for Di- (2-ethylhexyl) phthalate (DEHP), with detection limits of 4.5, 3.3, 11.6 and 13.1 ng/g, respectively. Using the Kruskal–Wallis statistical test, several associations were found between the measured phthalate and occupant activities (duration of ventilation and location of temporary garbage storage) and building characteristics (plastic or synthetic materials inside the houses). Full article

The intensification of global warming under the influence of human activities has directly led to an increase in the magnitude of changes in the climate system, further exacerbating the impact on the global water cycle and making extreme weather events more frequent and intense. In this study, daily temperature data from 1960 to 2020 from nine meteorological stations in the Qingyi River basin were used to analyze the changes of 16 extreme temperature indicators using extreme temperature indicators, the trend analysis method, and the MK analysis method. The results show that in terms of spatial distribution, the colder extreme events in the basin mainly occur in the upstream and that the warmer extreme events mainly occur in the midstream and downstream. According to the temporal trends of the indicators, the indicators of extreme events with anomalous warming are dominated by a significant upward trend, in which the warm night index increases by up to 3.8 days/decade, whereas the indicators of extreme events with anomalous cold are dominated by a significant downward trend, in which the cold night index decreases by up to −3.4 days/decade. In terms of the magnitude of change in the indicators, the cold and night indicators are more variable. According the spatial difference of index changes, the extreme value index mainly shows a decreasing trend in the upstream of the basin and an increasing trend in the midstream and downstream. Combined with the characteristic of a low extreme value index in the upstream and high extreme value index in the midstream and downstream of the basin as a whole, the spatial difference in the extreme value index in the basin is expected to further increase. Full article

El Niño-Southern Oscillation (ENSO) is a significant climate phenomenon on Earth due to its ability to change the global atmospheric circulation which influences temperature and precipitation across the globe. In this study, we investigate the responses of climatic and vegetation parameters due to two strong ENSO phases, i.e., La Niña (2010/2011) and El Niño (2015/2016) in South Africa. The study aims to understand the influence of strong seasonal ENSO events on climatic and vegetation parameters over South Africa. Remote sensing data from the Global Precipitation Measurement (GPM), Moderate Resolution Imaging Spectroradiometer (MODIS), Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) and Atmospheric Infrared Sounder (AIRS) was used. The relationship between precipitation, temperature, and Normalized Difference Vegetation Index (NDVI) were studied using Pearson’s correlation. Comparison between the La Niña, neutral year, and El Niño periods showed two interesting results: (1) higher precipitation from the south coast to the east coast of South Africa, with some low precipitation in the interior during the La Niña and El Niño periods, and (2) a drop in precipitation by ~46.6% was observed in the southwestern parts of South Africa during the La Niña and El Niño events. The study further showed that wind speed and wind direction were not impacted by strong ENSO events during the MAM, JJA and SON seasons, but the DJF season showed varying wind speeds, especially on the west coast, during both ENSO events. Overall, the Pearson’s correlation results clearly showed that the relationship between climatic parameters such as precipitation, temperature, and vegetation parameters such a NDVI is highly correlated while other parameters, such as wind speed and direction, are not. This study has provided new insights into the relationship between temperature, precipitation, and NDVI in South Africa; however, future work will include other climatic and vegetation parameters such as relative humidity and net longwave radiation. Full article

The iron and steel industry is an important foundation of the national economy. It is the inevitable choice, to achieve high-quality development in the new era of the iron and steel industry, to speed up the green development of the iron and steel industry. This paper studies the effect of steel industry agglomeration on regional economic growth and air pollution. Through the analysis of the characteristics of iron and steel industry agglomeration, and the empirical analysis of the relationship between iron and steel industry agglomeration, regional economic growth, and air pollution, it is found that: (1) Iron and steel industry agglomeration helps to promote economic growth; (2) Iron and steel industry agglomeration has an obvious spatial correlation effect and obviously drives the development of surrounding areas; (3) Iron and steel industry agglomeration will cause air pollution. The marginal effect of air pollution will decline rapidly with the development of iron and steel industry agglomeration. (4) The impact of green process innovation investment on air pollution presents an inverted U-shaped effect, which has a positive effect on air recovery after exceeding the critical point. (5) The air self-purification capacity represented by precipitation, helps to reduce air pollution. Based on the above conclusions, this paper puts forward some policy suggestions, such as making a scientific development plan for the iron and steel industry, accelerating green process innovation, effectively improving regional precipitation and precipitation times, vigorously promoting high-quality development of the regional economy, and comprehensively promoting coordinated development of the iron and steel industry, so as to cope with the dilemma of the coordinated development of the iron and steel industry, regional economic growth, and smog pollution, and strive for international development in the future. In the competition, we should gain the first opportunity and obvious competitive advantage. Full article

An important task in the construction of China’s ecological civilization, the selection and implementation of policy instruments fully reflect the actual effectiveness of the government’s efforts to control air pollution. Based on the content analysis method, this study examines the changing process of air pollution control policy instruments in southwest China from 2010 to 2021 in terms of implementation, synergy, and integration of policy instruments. The results show that, in terms of the degree of mandatory, the frequency of using policy instruments generally increased with time, but the overall balance of the instrument portfolio was poor. In terms of the degree of synergy, a gradual shift occurred from government-led to government-society governance. However, the concept and modes of inter-governmental linkage and cross-regional collaborative governance need to be improved. As for the degree of systemic, a clear trend of instrument integration and more frequent information interaction was found. Emergency-oriented characteristics appear strong, but a regular governance mechanism is lacking. Therefore, this paper provides policy suggestions and academic considerations for further improving the effectiveness of air pollution management in southwest China from three aspects: optimizing the policy tool system, deepening the regional joint prevention and control mechanism of air pollution, and promoting intelligent air pollution management. Full article

Remote sensing of ocean surface pressure from space is very important, and differential absorption LIDAR and differential absorption radar are only two kinds of remote sensing instruments with this potential. The differential absorption LIDAR works with the integral path mode from the spacecraft in the 400 km low-Earth orbit. The differential optical depth of the oxygen A-band is measured, and then the ocean surface pressure is obtained using a circle-iterative calculation. Performance evaluation of the differential absorption LIDAR model was based on feasibility to the advanced system parameters of the space instrument, whilst weak echo pulse energy at ocean surface yielded random errors in the surface pressure measurement. On the other hand, uncertain atmospheric temperature profiles and water vapor mixture profiles resulted in a primary systematic error in the surface pressure. The error of the surface pressure is sensitive to the jitter of the central frequency of laser emission. Under a strict implementation of the error budget, the time resolution is 6.25 s and the along-orbit distance resolution is 44 km, 625 echoes from ocean surface was cumulatively averaged. Consequently, if the jitter of the central frequency of laser emission exceeded 10 MHz, controlling the error of the surface pressure below 0.1% proved almost hopeless; further, the error could be expected to within 0.1–0.2%; however, the error limited within 0.2–0.3% is an achievable indicator. Full article

Based on high-resolution pressure data collected by a microbarograph and Fourier transform (FFT) data processing, a detailed analysis of the frequency spectra characteristics of gravity waves during a hailstone event in the cold vortex of Northeast China (NECV) on 9 September 2021 is presented. The results show that the deep NECV served as the large-scale circulation background for the hailstone event. The development of hailstones was closely related to gravity waves. In different hail stages, the frequency spectra characteristics of gravity waves were obviously different. One and a half hours before hailfall, there were gravity wave precursors with periods of 50–180 min and corresponding amplitudes ranging from 30 to 60 Pa. During hailfall, the center amplitudes of the gravity waves were approximately 50 Pa and 60 Pa, with the corresponding period ranges expanding to 60–70 min and 160–240 min. Simultaneously, hailstones initiated shorter periods (26–34 min) of gravity waves, with the amplitudes increasing to approximately 12–18 Pa. The relationship between hailstones and gravity waves was positive. After hailfall, gravity waves weakened and dissipated rapidly. As shown by the reconstructed gravity waves, key periods of gravity wave precursors ranged from 50–180 min, which preceded hailstones by several hours. When convection developed, there was thunderstorm high pressure and an outflow boundary. The airflow converged and diverged downstream, resulting in the formation of gravity waves and finally triggering hailfall. Gravity wave predecessors are significant for hail warnings and artificial hail suppression. Full article

In recent years, global warming has become increasingly severe, and the ecological and environmental problems facing mankind have become increasingly serious. As the main areas of transportation activities, cities are also the main places of carbon emissions. As a necessary condition for human’s daily-life travel, it is particularly important to calculate the carbon emissions from urban transportation. Due to the different characteristics of economy and population in different regions of a city, the carbon emissions of urban buses show different characteristics in terms of temporal and spatial distribution. The developments of science and technology promote the application of big data analysis to specific practical life, enabling people to research and solve problems from a new perspective. This paper uses the GPS data of urban buses in Sanya City, China, to identify operation conditions from urban buses, and calculates the distance and time under different conditions. Based on the measured data of carbon emissions, this paper visualizes the distribution characteristics of carbon emissions by density analysis; explains the time distribution characteristics by the visual analysis of carbon emissions in different time periods, working days and rest days, and different energy types; and illustrates the spatial distribution characteristics by the spatial distributions of carbon emissions from Sanya’s buses on working days and rest days, as well as in different routes, providing reference for a low-carbon development of urban green transport. Full article

No clear evidence shows the association between particulate matter (PM) with an aerodynamic diameter < 10 µm (PM₁₀) and asthma control. Therefore, the objective of this study was to determine the association between PM₁₀ and asthma control. A retrospective observational study was conducted at the Airway Clinic, Chiang Mai University Hospital, Chiang Mai, Thailand, between January 2010 and April 2013. Various values of asthma control test (ACT) scores between high and low PM₁₀ periods were analyzed. The association of an increased monthly average PM₁₀ level and ACT score was analyzed using a time series analysis. There were a total of 1180 visits from 236 asthmatic patients. The monthly average ACT score was significantly lower in the high PM₁₀ period compared with the low PM₁₀ period. Every 10 µg/m³ increment of monthly average PM₁₀ resulted in a significantly decreased ACT score at lag zero and one month, with an adjusted coefficient of –0.101 (95% CI; –0.165, –0.037), p-value = 0.002 and –0.079 (95% CI; –0.147, –0.012), p-value = 0.021, respectively. Monthly average PM₁₀ significantly affected asthma control in asthmatic patients. During the air pollution period, the serial assessments of ACT should be measured for early detection of worsening asthma control. Full article

Since the mid-1990s, there has been a marked decrease in the sea ice extent (SIE) in the Arctic Ocean. After reaching an absolute minimum in September 2012, the seasonal variations in the SIE have settled at a new level, which is almost one-quarter lower than the average climatic norm of 1979–2022. Increased melting and accelerated ice export from marginal seas ensure an increase in the open water area, which affects the lower atmosphere and the surface layer of the ocean. Scientists are cautiously predicting a transition to a seasonally ice-free Arctic Ocean as early as the middle of this century, which is about 50 years earlier than was predicted just a few years ago. Such predictions are based on the fact that the decrease in sea ice extent and ice thinning that occurred at the beginning of this century, initially caused by an increase in air temperature, triggered an increase in the thermal and dynamic contribution of the ocean to the further reduction in the ice cover. This paper reviews published evidence of such changes and discusses possible mechanisms behind the observed regional anomalies of the Arctic Sea ice cover parameters in the last decade. Full article

The Saudi government targets building eight solar plants across the country by 2030, which are expected to produce more than 3600 MW, enough to power more than 500,000 homes. However, the vast desert environment in Saudi Arabia increases dust and aerosol loading in the atmosphere, which affect the performance of photovoltaic systems due to scattering and absorption of the solar radiation by dust particles. In this work, ground-based data from weather stations located in six Saudi cities, Dammam, Hafar Al Batin, Riyadh, Jeddah, Najran, and Arar, along with data from the Moderate Resolution Imaging Spectroradiometer (MODIS) are used to examine the effects of dust loading on aerosol optical parameters, air temperature, and solar irradiance. The effects of three major dust storms that blew over different regions in Saudi Arabia on 20 March 2017, 23 April 2018, and 15 April 2021 have been investigated. It is found that there is a strong correlation between dust loading and aerosol optical parameters. The maximum Aerosol Optical Depth (AOD) was recorded over Jeddah on 19 March 2017 (about 2), over Riyadh on 20 March 2017 (about 2.3), over Riyadh on 24 April 2018 (about 1.5), and over Najran on 15 April 2021 (about 0.9). Strong dust events are found to reduce air temperature by a few degrees in high dust loading regions. The study found that such large dust loading decreases the direct and global solar irradiance components, while it increases the diffuse component over the cities of Jeddah, Riyadh, and Najran. This could be an indication that scattering from dust particles can play a significant role in the solar irradiance intensity. Full article

Malaysia has been facing transboundary haze events repeatedly, in which the air contains extremely high particulate matter, particularly PM10, which affects human health and the environment. Therefore, it is crucial to understand the characteristics of PM10 concentration and develop a reliable PM10 forecasting model for early information and warning alerts to the responsible parties in order for them to mitigate and plan precautionary measures during such events. This study aims to analyze PM10 variation and investigate the performance of quantile regression in predicting the next-day, the next two days, and the next three days of PM10 levels during a high particulate event. Hourly secondary data of trace gases and the weather parameters at Pasir Gudang, Melaka, and Petaling Jaya during historical haze events in 1997, 2005, 2013, and 2015. The Pearson correlation was calculated to find the correlation between PM10 level and other parameters. Moderate correlated parameters (r > 0.3) with PM10 concentration were used to develop a Pearson–QR model with percentiles of 0.25, 0.50, and 0.75 and were compared using quantile regression (QR) and multiple linear regression (MLR). Several performance indicators, namely mean absolute error (MAE), root mean squared error (RMSE), coefficient of determination (R²), and index of agreement (IA), were calculated to evaluate and compare the performances of the predictive model. The highest daily average of PM10 concentration was monitored in Melaka within the range of 69.7 and 83.3 µg/m³. CO and temperature were the most significant parameters associated with PM10 level during haze conditions. Quantile regression at p = 0.75 shows high efficiency in predicting PM10 level during haze events, especially for the short-term prediction in Melaka and Petaling Jaya, with an R² value of >0.85. Thus, the QR model has high potential to be developed as an effective method for forecasting air pollutant levels, especially during unusual atmospheric conditions when the overall mean of the air pollutant level is not suitable for use as a model. Full article

Atmospheric synoptic weather patterns have a significant impact on the concentration, dispersion, and transportation of air pollution in various regions and times around the world. To assess the impact of atmospheric synoptic weather patterns and long-range air mass transportation, we used weather classification techniques from the BP training model and the HYSPLIT model. Our research uncovered four weather conditions linked to PM₁₀ concentration categories ranging from normal to extreme. Weather conditions 3 and 4 are the most significant conditions supporting the occurrence of extreme concentration events that are heavily influenced by anti-cyclones. Despite weather conditions influencing high concentrations, 60% of long-distance air mass transport to Secunda from Mpumalanga province increased to extreme PM₁₀ concentrations. Furthermore, long-term weather shifts have been observed to positively impact reducing the concentration of PM₁₀ extreme events. Full article

Deep learning models have been widely used in time-series numerical prediction of atmospheric environmental quality. The fundamental feature of this application is to discover the correlation between influencing factors and target parameters through a deep network structure. These relationships in original data are affected by several different frequency factors. If the deep network is adopted without guidance, these correlations may be masked by entangled multifrequency data, which will cause the problem of insufficient correlation feature extraction and difficult model interpretation. Because the wavelet transform has the ability to separate these entangled multifrequency data, and these correlations can be extracted by deep learning methods, a hybrid model combining wavelet transform and transformer-like (WTformer) was designed to extract time–frequency domain features and prediction of air quality. The 2018–2021 hourly data in Guilin was used as the benchmark training dataset. Pollutants and meteorological variables in the local dataset are decomposed into five frequency bands by wavelet. The analysis of the WTformer model showed that particulate matter (PM_2.5 and PM₁₀) had an obvious correlation in the low-frequency band and a low correlation in the high-frequency band. PM_2.5 and temperature had a negative correlation in the high-frequency band and an obvious positive correlation in the low-frequency band. PM_2.5 and wind speed had a low correlation in the high-frequency band and an obvious negative correlation in the low-frequency band. These results showed that the laws of variables in the time–frequency domain could be found by the model, which made it possible to explain the model. The experimental results show that the prediction performance of the established model was better than that of multilayer perceptron (MLP), one-dimensional convolutional neural network (1D-CNN), gate recurrent unit (GRU), long short-term memory (LSTM) and Transformer, in all time steps (1, 4, 8, 24 and 48 h). Full article

It is of great significance to determine the level of demand for thermal environment regulation and the availability of blue–green spaces for thermal environment regulation to alleviate the effects of urban heat islands. Taking Shandong Province, China, as the study area, combined multi–source remote sensing data are used in this study to construct the index system of cold island supply capacity (CIS) and the cold island demand level (CID). We use the methods of spatial regression, quadrant division, and coupling coordination degree to analyze the correlation, matching status, and the level of coordinated development between the supply capacity and demand for the cooling effect. We also explore the change law and spatial characteristics of the blue–green spaces’ cooling effects supply and demand matching. Results show that: (1) The CIS and the CID are significantly negatively correlated and spatially heterogeneous in distribution, with a significant spatial spillover effect. (2) The dominant type of supply and demand is one of low supply and high demand, which means that the supply and demand for cool islands’ cooling effect are unbalanced, with significant problems of spatial mismatch and quantitative imbalance. (3) The coupling between supply capacity and demand level is on the verge of becoming dysfunctional because the uneven distribution of urban buildings, population, and blue–green spaces reduce the coupling between supply and demand levels. This research can provide a new perspective and scientific basis for the study of the cooling effects of blue and green spaces and the mitigation of the heat island effect in densely populated urban centers. Full article

Comprehensive morphological and mineralogical studies of atmospheric microparticles sampled on the roof of the museum complex and near roads in the town of Istra, Moscow region, have been carried out. Morphological research at different hierarchical levels revealed the multicomponent composition of microparticles and made it possible to identify the most characteristic groups of microparticles of natural and anthropogenic origin. The composition of the studied atmospheric microparticles is dominated by mineral grains of quartz and feldspars; biotite and calcite are singly noted, which reflects the ecological and geographical conditions of their formation, namely the Central Russian mineralogical province. A small share of technogenic particles in the composition of aerosol fallout indicates a low level of technogenic load and a favorable environmental situation in the study area, largely due to the protective functions of the forest park zone. The results of determining the material composition and calculating the enrichment factors also indicate a low level of technogenic impact on the natural environment. Full article

The Southern Ocean (SO) and Antarctica play important roles in the global climate. The new particle formation (NPF) alters the availability of cloud condensation nuclei (CCN), leading to impacts on the cloud reflectance and global radiative budget. In this review, we introduce the common instruments for measuring particle number concentration (PNC) and particle number size distribution (PNSD). Based on the observations over the Antarctic and some Antarctic research stations, we explored spatial and temporal characteristics of PNCs and PNSDs. From the SO to the interior of the Antarctic, the total PNCs show a decreasing trend, and the total PNCs present an obvious seasonal cycle, with the low concentration in winter (June–August) and the high concentration in summer (December–February). By summarizing the research progress over the SO and Antarctica, we discuss possible precursors of the NPF: sulfuric acid (H₂SO₄, SA), methanesulfonic acid (CH₃S(O)₂OH, MSA), dimethyl sulfide ((CH₃)₂S, DMS), iodic acid (HIO₃, IA), iodous acid (HIO₂), ammonia (NH₃), dimethylamine ((CH₃)₂NH, DMA), highly oxygenated organic molecules (HOMs) and other organics with low vapor pressure. We also explore several possible nucleation mechanisms: ion-induced nucleation of H₂SO₄ and NH₃, H₂SO₄-amines, H₂SO₄-DMA-H₂O, H₂SO₄-MSA-DMA, IA-MSA, IA-DMA, heterogeneous IA-organics nucleation mechanisms and environmental conditions required for the NPF. NPF is one of the main sources of CCN in the remote marine boundary layer, such as the SO and Antarctica. Thus, we discuss the contribution of NPF to CCN and the indirect impacts of NPF on climate. Through this review, we could better understand the PNC and NPF over the SO and Antarctica and their impacts on the global climate. Full article

Air pollution is a major public health threat that is associated with asthma, cardiovascular disease, respiratory disease and all-cause mortality. Among the most important acute air pollution events occurring each year are celebrations involving fireworks, such as the 4th of July holiday in the United States. In this community-engaged study, academic partners and residents collaborated to collect indoor and outdoor PM_2.5 concentration measurements in the disadvantaged city of Santa Ana, California, using low-cost AtmoTube sensor devices before, during and after the July 4th firework celebration, while also examining July 4th data extracted from the PurpleAir sensor network across over a hundred other cities in southern California. Average outdoor PM_2.5 concentrations on July 4th were found to be three-to-five times higher than baseline, with hourly concentrations exceeding 160 μg/m³. Outdoor averages were roughly 30% to 100% higher than indoor levels. The most polluted cities exhibited 15-times higher PM_2.5 levels compared with the least contaminated cities and were often those where household-level fireworks were legal for sale and use. Race/ethnicity was found to be the leading predictor of July 4th-related air pollution across three counties in southern California, with greater PM_2.5 being associated with higher proportions of Hispanic residents and lower proportions of White residents. The findings from this study underscore the importance of environmental justice as it relates to firework-related air pollution exposure, and the critical role city- and county-level firework policies play in determining exposure. Full article

This paper explored the changes of six significant pollutants (PM_2.5, PM₁₀, SO₂, NO₂, O₃, and CO) in Jilin City during the coronavirus disease 2019 (COVID-19) epidemic in 2022, and compared them with the same period of previous years to analyze the impact of anthropogenic emissions on the concentration of pollutants; The Weather Research and Forecasting Community Multiscale Air Quality (WRF–CMAQ) model was used to evaluate the effect of meteorological factors on pollutant concentration. The results showed that except for O₃, the concentrations of the other five pollutants decreased significantly, with a range of 21–47%, during the lockdown period caused by the government’s shutdown and travel restrictions. Compared with the same period in 2021, the decrease of PM_2.5 was only 25% of PM₁₀. That was because there was still a large amount of PM_2.5 produced by coal-fired heating during the blockade period, which made the decrease of PM_2.5 more minor. A heavy pollution event caused by adverse meteorological conditions was found during the lockdown period, indicating that only controlling artificial emissions cannot eliminate the occurrence of severe pollution events. The WRF–CMAQ results showed that the lower pollutant concentration in 2022 was not only caused by the reduction of anthropogenic emissions but also related to the influence of favorable meteorological factors (higher planetary boundary layer thickness, higher wind speed, and higher temperature). Full article

One of the key challenges in using fossil fuels is the environmental impacts of these energy sources, and to reduce these destructive effects, the use of renewable energy sources (biofuels) is necessary. One of the important biofuels is biodiesel, which can be produced from cottonseed. To properly manage the chain dealing with biodiesel production from the cottonseed chain (from farm to manufactory), environmental hotspots must be pinpointed. In the present study, it was attempted to examine the environmental impacts of the biodiesel production cycle from cottonseed (agronomic stages, ginning, oiling, and biodiesel production). The data obtained in all three stages were analyzed by the Impact 2002+ method in the SimaPro software. The highest contribution to creating environmental indicators at the agricultural stage was related to the use of nitrogen fertilizers, direct emission from the farm and fossil fuels, the ginning and oiling stage involving the use of diesel fuel and sulfuric acid, and the production of biodiesel in the manufactory involving the use of methanol and electricity. The potential environmental impacts of a functional unit of 1 kg of biodiesel include: human health, 9.05–10⁻⁶ (DAYLY); ecosystem quality, 1.369 (PDF*m²*year); climate changes, (kg CO₂ eq.) 17.247; and resources (MJ primary), 89.116. Results showed that agriculture has more significant participation in the environmental impact than other sections (ginning and oiling and biodiesel production), especially due to the application of fertilizers and fuel. Surveying the environmental indicators of the results showed that at the agricultural stage, the human health indicator is 10.43, 1.21, and 5.32 times higher than the ecosystem quality, climate change, and resource indicators, respectively; at the ginning and oiling stages, it is 2.35, 31.68, and 2.09 times higher, respectively; and at the stage of biodiesel production in the manufactory, it is 16.41, 1.96, and 0.99 times higher, respectively, in terms of the destructive effects. The overall results showed that the hotspot points in the present study can be largely modified by reducing the consumption of nitrogen fertilizers, using new equipment and machinery, ginning and oiling, and using fewer methanol ratios than oil. Full article

This work is devoted to the development of an ensemble of below-cloud scavenging models of pollutant aerosol transport into the atmosphere. Among other factors contributing to the uncertainty of the forecasts of the dispersion and deposition of technogenic gas-aerosol releases in the atmosphere, precipitation scavenging is one of the least studied and, in case of precipitation, can be the dominant mechanism for aerosol deposition. To form the ensemble of below-cloud scavenging models, appropriate experimental data, raindrop-aerosol capture models, raindrop terminal velocity parameterizations, and raindrop size distributions were chosen. The pool of models was prepared and then evaluated to adequately describe the experimental data using statistical analysis. Rank diagrams were used to analyze the adequacy of meteorological ensembles; together with the ensemble distribution construction, they allowed selecting the groups of models with such properties as to produce unbiased estimates and dispersion corresponding to the dispersion of the experimental data. The model calculations of the concentration fraction deposited due to below-cloud scavenging were performed using a log-normal distribution with characteristics corresponding to those observed during the accidents at the Chernobyl NPP and Fukushima-1 NPP. The results were compared with those obtained using the models of the NAME and FLEXPART codes. The results of this work can be used to improve the current approaches applied for modelling the distribution of pollutants in the atmosphere in the case of emergency, enhancing the reliability of forecasts by taking into account uncertainties in the results. The formed multi-model ensemble will be included in the decision support system used in responding to releases of radioactive substances into the atmosphere. Full article

The Pacific Walker circulation (PWC) significantly affects the global weather patterns, the distribution of mean precipitation, and modulates the rate of global warming. In this study, we review and compare 10 different indices measuring the strength of the PWC using data from the ERA5 reanalyses for the period 1951–2020. We propose a revised velocity potential index, while we also discuss two streamfunction indices. We show that the normalized PWC indices largely agree on the annual-mean strength of the PWC, with the highest correlations observed between indices that measure closely linked physical processes. The indices tend to disagree the most during the periods of strong El Niño. Therefore, the trends in PWC strength vary depending on the chosen time frame. While trends for 1981–2010 show PWC strengthening, trends for 1951–2020 are mostly neutral, and the recent trends (2000–2020) show (insignificant) weakening of the PWC. The results hint at the multidecadal variability in the PWC strength with a period of approximately 35 years, which would result in continued weakening of the PWC in the coming decade. Full article

Solid precipitation falling near 0 °C, mainly snow, can adhere to surface features and produce major impacts. This study is concerned with characterizing this precipitation over the Canadian Prairie provinces of Manitoba and Saskatchewan in the current (2000–2013) and pseudo-global warming future climate, with an average 5.9 °C temperature increase, through the use of high resolution (4 km) model simulations. On average, simulations in the current climate suggest that this precipitation occurs within 11 events per year, lasting 33.6 h in total and producing 27.5 mm melted equivalent, but there are wide spatial variations that are partly due to enhancements arising from its relatively low terrain. Within the warmer climate, average values generally increase, and spatial patterns shift somewhat. This precipitation consists of four categories covering its occurrence just below and just above a wet-bulb temperature of 0 °C, and with or without liquid precipitation. It generally peaks in March or April, as well as in October, and these peaks move towards mid-winter by approximately one month within the warmer climate. Storms producing this precipitation generally produce winds with a northerly component during or shortly after the precipitation; these winds contribute to further damage. Overall, this study has determined the features of and expected changes to adhering precipitation across this region. Full article

Wind power has great potential in the fields of electricity generation, heating, et cetera, and the precise forecasting of wind speed has become the key task in an effort to improve the efficiency of wind energy development. Nowadays, many existing studies have investigated wind speed prediction, but they often simply preprocess raw data and also ignore the nonlinear features in the residual part, which should be given special treatment for more accurate forecasting. Meanwhile, the mainstream in this field is point prediction which cannot show the potential uncertainty of predicted values. Therefore, this paper develops a two-stage decomposition ensemble interval prediction model. The original wind speed series is firstly decomposed using a complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and the decomposed subseries with the highest approximate entropy is secondly decomposed through singular-spectrum analysis (SSA) to further reduce the complexity of the data. After two-stage decomposition, auto-encoder dimensionality reduction is employed to alleviate the accumulated error problem. Then, each reconstructed subsequence will generate an independent prediction result using an elastic neural network. Extreme gradient boosting (Xgboost) is utilized to integrate the separate predicted values and also carry out the error correction. Finally, the Gaussian process (GP) will generate the interval prediction result. The case study shows the best performance of the proposed models, not only in point prediction but also in interval prediction. Full article

Elevated heart rate (HR) can be hypothesized to be involved in the pathways by which ambient air pollution, especially fine particulate matter (PM_2.5), causes cardiovascular morbidity and mortality. However, evidence concerning long-term PM_2.5 exposure and HR is still limited. Therefore, in this study, we assessed the associations of PM_2.5 with HR levels and tachycardia prevalence and explored potential modifiers of the associations. We used baseline data of 1,802,207 adults from the Kashgar Prospective Cohort Study (KPCS). PM_2.5 exposure was assessed based on satellite sensing data, meteorological factors, multi-resolution emission inventory, and measurements from ground-based surface monitors measurements. HR was measured using a calibrated electronic sphygmomanometer, and tachycardia was defined as resting heart rate (RHR) equal to or greater than 80 beats per minute. Linear regression and logistic regression models were employed to evaluate the associations of PM_2.5 levels with RHR levels and tachycardia prevalence, respectively. Stratified analyses by sex, age, ethnicity, smoking status, alcohol use, and physical activity were also performed. The mean (standard deviation) age of the study participants was 39.4 (15.5) years old. In the adjusted models, an interquartile range (8.8 µg/m³) increase in PM_2.5 levels was associated with 0.515 (95% confidence interval: 0.503–0.526) bpm increase in RHR levels and with 1.062-fold (95% confidence interval: 1.059–1.064) increase in the odds of tachycardia. The results were robust against several sensitivity analyses. In addition, we observed the above associations were stronger in participants that were men, of Uyghur ethnicity, smoking cigarettes, drinking alcohol, and having physical inactivity, compared to their counterparts. In summary, our findings indicate that long-term exposure to ambient PM_2.5 may be hazardously associated with HR, and women, Uyghur people, and those with unhealthy lifestyles may be more vulnerable to the hazardous effects. Full article

Accurate assessments of exposure to urban air pollution with higher traffic emissions and its health risks still face several challenges, such as intensive computation of air pollution modeling and the limited availability of personal activity data. The macroscopic health effects can be transmitted to the whole population for personal prevention via air quality health index (AQHI), but the possibility risk index of the specific allergic diseases is still lacking. This interdisciplinary study aims at evaluating the forecasted results of high-resolution air quality with updated traffic emissions and accessing the potential impacts of outdoor pollution on morbidity of rhinitis for urban residents. A high-resolution modelling system (1 km × 1 km) containing the online traffic emission model (VEIN), meteorological and air quality model (WRF-CHIMERE) and the health impact module was developed. A new health index of Potential Morbidity Risk Index (PMRI) was further established using higher resolution health risk coefficients of major air pollutants on allergic rhinitis, and different methods (with/without considering population distributions) targeting different user groups (residents, hospitals and health administrations) were calculated and analyzed. Operational forecasted results of hourly PMRI can be further combined with online map services to serve as an effective tool for patients with allergic rhinitis to arrange their daily activities so as to avoid acute exacerbation. The forecasted PMRIs accessible to the public will also be beneficial for the public health administrations in planning the medical resource and improving the outpatient efficiency. Full article

Monoterpenes significantly affect air quality and climate as they participate in tropospheric ozone formation, new particle formation (NPF), and growth through their oxidation products. Vegetation is responsible for most biogenic volatile organic compound (BVOC) emissions released into the atmosphere, yet the contribution of shrub and regional transport to the ambient monoterpene mixing ratios is not sufficiently documented. In this study, we present one-year systematic observations of monoterpenes in the Eastern Mediterranean at a remote coastal site, affected mainly by the typical phrygana vegetation found on the Island of Crete in Greece. A total of 345 air samples were collected in absorption tubes and analyzed by a GC-FID system during three intensive campaigns (in spring 2014, summer 2014, and spring 2015) in addition to the systematic collection of one diurnal cycle per week from October 2014 to April 2015. Limonene, α-pinene and 1,8-cineol have been detected. The mixing ratios of α-pinene during spring and summer show a cycle that is typical for biogenic compounds, with high levels during the night and early morning, followed by an abrupt decrease around midday, which results from the strong photochemical depletion of this compound. Limonene was the most abundant monoterpene, with average mixing ratios of 36.3 ± 66 ppt. The highest mixing ratios were observed during autumn and spring, with a maximum mixing ratio in the early afternoon. The spring and autumn maxima could be attributed to the seasonal behavior of vegetation growth at Finokalia. The green period starts in late autumn when phrygana vegetation grows because of the rainfall; the temperature is still high at this time, as Finokalia is located in the southeast part of Europe. Statistical analyses of the observations showed that limonene and α-pinene have different sources, and none of the studied monoterpenes is correlated with the anthropogenic sources. Finally, the seasonality of the new particle formation (NPF) events and monoterpene mixing ratios show similarities, with a maximum occurring in spring, indicating that monoterpenes may contribute to the production of new particles. Full article

We performed an analysis of year-to-year variations in the characteristics of the upper neutral atmosphere and the ionosphere over Eastern Siberia. The mesopause temperature (Tm) obtained from the spectrometric observations of the OH(6-2) emission and the peak electron density (NmF2) from the ionosonde measurements were used as atmospheric and ionospheric characteristics. We considered the annual mean Tm and yearly average values of NmF2, as well as yearly average values of day-to-day and intradiurnal variability in Tm and NmF2. To interpret the year-to-year variations, we use multiple regressions of the ionospheric and atmospheric characteristics on the F10.7-index (as a proxy of solar activity) and Ap-index (as a proxy of geomagnetic activity). For the atmospheric characteristics, we also used regressions on the SOI index (as a proxy of circulation in the lower atmosphere). The yearly average values of NmF2 are dominantly controlled by changes in the solar flux. The year-to-year variations in the NmF2 variability are mainly driven by changes in both solar and geomagnetic activity. The year-to-year variations in the mesopause temperature weakly correlate with changes in the indices of solar and geomagnetic activity. The yearly average values of Tm variability correlate with changes in the SOI-index: the day-to-day variability demonstrates a positive correlation with the SOI-index, while the intradiurnal variability shows a negative correlation with the SOI-index. The study did not reveal a significant relationship between the year-to-year variations in the NmF2 variability and Tm variability. Full article

During the autumn season, Sicily is often affected by severe weather events, such as self-healing storms called V-shaped storms. These phenomena cause significant total rainfall quantities in short time intervals in localized spatial areas. In this framework, this study analyzes the meteorological event recorded on 11–12 November 2019 in Sicily (southern Italy), using the Weather Research and Forecasting (WRF) model with a horizontal spatial grid resolution of 3 km. It is important to note that, in this event, the most significant rainfall accumulations were recorded in eastern Sicily. In particular, the weather station of Linguaglossa North Etna (Catania) recorded a total rainfall of 293.6 mm. The precipitation forecasting provided by the WRF model simulation has been compared with the data recorded by the meteorological stations located in Sicily. In addition, a further simulation was carried out using the Four-Dimensional Data Assimilation (FDDA) technique to improve the model capability in the event reproduction. In this regard, in order to evaluate which approach provides the best performance (with or without FDDA), the Root Mean Square Error (RMSE) and dichotomous indexes (Accuracy, Threat Score, BIAS, Probability of Detection, and False Alarm Rate) were calculated. Full article

It is well known that air pollution has a negative impact on human health. Research has shown an increasing trend in hospital admissions due to respiratory and heart diseases during and after consecutive days of high or even medium air pollution levels. The objective of this paper is to provide quantitative and qualitative data concerning the impact of long-term air pollution on the health of residents living in the Greater Athens Area (GAA). More accurately, the prevalence of bronchitis in children and the incidence of chronic bronchitis cases in adults due to particulate matter exposure are estimated utilizing the AirQ+ model. For this purpose, daily average concentrations of particulate matter with an aerodynamic diameter less than or equal to 10 μm (PM₁₀) from five different locations within the GAA, covering the period 2001–2020, are used. The results show a significant correlation between PM₁₀ concentrations and adverse health effects (R² = 0.9). Interestingly, there were more cases of children suffering from bronchitis disease than cases of adults. In addition, it was observed that the unhealthiest areas in the GAA are the center of Athens city (mean annual PM₁₀ concentration in 2019: 36 μgr/m³), as well as suburban areas (Lykovrissi and Marousi: mean annual PM₁₀ concentrations in 2019 were 27 μgr/m³ and 28 μgr/m³, respectively). Finally, a decreasing trend for both PM₁₀ concentrations and the prevalence of chronic bronchitis across the GAA was observed through the examined 20 years, which was significantly higher over the period 2010–2020. Full article