Variations In Meteorological Conditions Research Articles

A study of the relation between traffic accidents and election posters

Inattention in traffic is a risk factor for the occurrence of accidents. Elements surrounding traffic could potentially be a safety risk for road users. This study investigates the relation between election posters and traffic accidents using a seasonal autoregressive-moving-average model with exogeneous variables (seasonal ARMAX) correcting for variations in meteorological conditions and general traffic volume indicated through public holidays. Danish traffic accident micro data are used to construct a time series for daily number of accidents in city zones adjusted for probable known accident causes, namely drunk driving, narcotics, drivers deemed unfit to drive, and accidents related to low visibility. Model parameters are determined from Maximum Likelihood estimation on the period from 1 January 1997 to 15 December 2009 with an indicator for general election periods. The presence of general election posters is associated with an increased risk of traffic accidents (p = 0.003), when correcting for meteorological variables and public school holidays. While a causal relation between traffic accidents and the presence of election posters cannot be concluded from the analysis, we find distraction from election posters to be the most likely explanation for the associated increase in traffic accidents. On average, the model indicates that 1.18 additional daily accidents occur for general elections in Denmark. In total, that amounts to 127 additional accidents for the investigated period of time. This study shows that such sudden changes in traffic surroundings are significantly related to increased safety risks which could lead to a better discussion of relevant traffic legislation.

Learning-based Pareto-optimum routing of ships incorporating uncertain meteorological and oceanographic forecasts

In modern shipping logistics, multi-objective ship route planning has attracted considerable attention in both academia and industry, with a primary focus on energy conservation and emission reduction. The core challenges in this field involve determining the optimal route and sailing speed for a given voyage under complex and variable meteorological and oceanographic conditions. Typically, the objectives revolve around optimizing fuel consumption, carbon emissions, duration time, energy efficiency, and other relevant factors. However, in the multi-objective route planning problem involving variable routes and speeds, the extensive solution space contains a substantial number of unevenly distributed feasible samples. Traditional heuristic optimization techniques, such as multi-objective evolutionary algorithms, which serve as the core component of optimization programs, suffer from inefficiencies in exploring the solution space. Consequently, these algorithms may tend to converge toward local optima during population iteration, resulting in a solution set characterized by sub-optimal convergence and limited diversity. This ultimately undermines the potential benefits of routing optimization. To address such challenging problem in route planning tasks, we propose a self-adaptive intelligent learning network aiming at capturing the potential evolutionary characteristics during population iteration, in order to achieve high-efficiency directed optimization of individuals. Additionally, an uncertainty-driven module is developed by incorporating ensemble forecasts of meteorological and oceanographic variables to form the Pareto frontier with more reliable solutions. Finally, the overall framework of the proposed learning-based multi-objective evolutionary algorithm is meticulously designed and validated through comprehensive analyses. Optimization results demonstrate its superiority in generating routing plans that effectively minimize costs, reduce emissions, and mitigate risks.

Assessment of aerosol radioactivity in the atmospheric air of populated areas

Introduction. Due to the tense situation in the world, which may lead to deliberate emergencies at radiation-hazardous facilities as a result of terrorist or sabotage actions, the availability of methods for operational control of radioactive aerosols in the atmospheric air, as well as knowledge of background levels of α- and β-activity of aerosols in the locations of nuclear power facilities, mining and processing facilities The aim of the study was to assess the radioactivity of aerosols in the atmospheric air according to the indices of α- and β-activity (using the example of the cities of Voronezh, Novovoronezh, Pavlovsk, Lipetsk, Stary Oskol). Materials and methods. Atmospheric air sampling was carried out using a flow meter-sampler of PU-5 aerosol mixtures. The α- and β-activity of the surface of the AFA-RSP-20 filters was measured with the MKS-AT1117M dosimeter-radiometer with interchangeable detection units BDPA-01 and BDPB-01. Results. The results of measurements of aerosols α- and β-activity in the atmospheric air in all control points in the territories of populated places established the α-activity of the aerosols to vary from 1.10 • 10–3 to 6.75 • 10–3 Bq/m3, total (total) β-activity – from 3. 49 • 10–3 to 2.12 • 10–2 Bq/m3. The maximum indices values took place at control points near the open-pit mining of the Shkurlatovsky granite deposit (near Pavlovsk, Voronezh region). Limitations. The limitations of the study are related to possible errors of the proposed and tested methodology, single (one-time) measurements, variable meteorological conditions affecting a specific result. Conclusion. The maximum values of aerosols α- and β-activity in the atmospheric air of populated areas are determined by the results of one-time measurements (6.75 • 10–3 Bq/m3 and 2.12 • 10–2 Bq/m3) do not cause concerns for the health of the living population.

Divergent rainfall infiltration patterns on the Chinese Loess Plateau between growing and non-growing seasons after long-term revegetation

Rainfall infiltration into hillslopes on the Chinese Loess Plateau (CLP) undergoes noticeable changes during the growing season following long-term revegetation. However, infiltration patterns during the non-growing season have been understudied. In this study, we monitored soil moisture over three years (May 2016-December 2018) at 10-minute intervals, spanning five soil depths (10, 20, 40, 60, and 100 cm). Monitoring was conducted in both a forestland catchment and an adjacent grassland catchment on the CLP, with two monitoring sites per catchment: one downhill in the gully, and the other uphill on the slope. Our findings reveal pronounced seasonal variations in meteorological conditions and vegetation index. We observed divergent rainfall infiltration patterns and their governing factors between the growing (May to October) and non-growing seasons (November to April). During the growing season, rainfall facilitated a quicker response with shorter lag time (23.34 h vs. 56.84 h in the non-growing season) and noticeable soil moisture increments (up to 35.7 % cm3/cm3 vs. only up to 6.4 % cm3/cm3). The wetting depth often extended to 40–100 cm (compared to 10–40 cm in the non-growing season), with a higher wetting front velocity (17.44 mm/h vs. 6.24 mm/h) and an increased occurrence frequency of preferential flow (19.77 % vs. 9.72 %). The impact of rainfall amount and peak intensity on infiltration was more pronounced during the non-growing season, whereas, in the growing season, infiltration was additionally influenced by antecedent moisture conditions and vegetation type. Notably, in the growing season, the enhanced soil moisture recharge due to rainfall infiltration exceeded the rise in evapotranspiration, resulting in higher soil water storage. The findings of this study shed new light on seasonal infiltration dynamics on the greening CLP, contributing valuable insights into hillslope hydropedological processes post-revegetation.

Assessing the Impact of Climate Comfort on Labor Productivity in Hydropower Engineering Construction in Southwest China

Labor productivity exerts a significant influence on the construction cycle and investment in hydropower projects. Consequently, it is a crucial factor in the estimation of the cost of hydropower project construction. The mechanisms that are modulated by subjective factors have been extensively studied. However, the assessment of labor productivity in objective environments with regular changes is usually considered using a single factor, especially in special environments where the influence of environmental factors is of greater concern. As the most extensive region of China’s water-energy resources, the Southwest region has a lengthy tradition of using altitude or oxygen as an inherent criterion for the assessment of labor productivity. However, the applicability of inherent standards is limited. Therefore, we tried to assess the spatial and temporal changes in labor productivity based on the changes in meteorological conditions in Southwest China, employing climate comfort indicators in this study. The result identified five distinct regions of climate change in Southwest China (R-I to R-V). In particular, there is minimal variation in labor productivity as assessed by climate comfort indicators and the elevation between R-I and R-II. R-III and R-IV are influenced by oxygen, temperature, and humidity, while R-V is affected by a multitude of factors. Our findings indicate that temporal and spatial variations in meteorological conditions can result in up to a threefold difference in productivity at the same altitude in different regions. Importantly, our study provides valuable theoretical insights for engineering project management. In particular, it can be concluded that altitude is not a reliable indicator for evaluating labor productivity in high-altitude construction.

Response of PM2.5 chemical composition to the emission reduction and meteorological variation during the COVID-19 lockdown

PM2.5 is a main atmospheric pollutant with various sources and complex chemical compositions, which are influenced by various factors, such as anthropogenic emissions (AE) and meteorological conditions (MC). MC have a significant impacts on variations in atmospheric pollutant; therefore, emission reduction policies and ambient air quality are non-linearly correlated, which hinders the accurate assessment of the effectiveness of control measures. In this study, we conducted online observations of PM2.5 and its chemical composition in Hohhot, China, from December 1, 2019, to February 29, 2020, to investigate how the chemical compositions of PM2.5 respond to the variations in AE and MC. Moreover, the random forest (RF) model was used to quantify the contributions of AE and MC to PM2.5 and its chemical composition during severe hazes and the COVID-19 pandemic lockdown period. During the clean period, MC reduced PM2.5 concentrations by 124%, while MC incresed PM2.5 concentrations by 49% during severe pollution episode. Inorganic aerosols (SO42−, NO3−, and NH4+) showed the strongest response to MC. MC significantly contributed to PM2.5 (36%), SO42− (32%), NO3− (29%), NH4+ (28%), OC (22%), and SOC (17%) levels during pollution episodes. From the pre-lockdown to lockdown period, AE (MC) contributed 52% (48%), 81% (19%), 48% (52%), 68% (32%), 59% (41%), and 288% (−188%) to the PM2.5, SO42−, NO3−, NH4+, OC, and SOC reductions, respectively. The variations in MC (especially the increase in relative humidity) rapidly generated meteorologically sensitive species (SO42−, NO3−, and NH4+), which led to severe winter pollution. This study provides a reference for assessing the net benefits of emission reduction measures for PM2.5 and its chemical compositions.

Characterization of winter PM2.5 source contributions and impacts of meteorological conditions and anthropogenic emission changes in the Sichuan Basin, 2002–2020

In this study, the Weather Research and Forecasting (WRF) model and Community Multiscale Air Quality–Integrated Source Apportionment Method (CMAQ–ISAM) were utilized, which were integrated with the Multiresolution Emission Inventory for China (MEIC) emission inventory, to simulate winter PM2.5 concentrations, regional transport, and changes in emission source contributions in the Sichuan basin (SCB) from 2002 to 2020, considering variations in meteorological conditions and anthropogenic emissions. The results indicated a gradual decrease in the basin's winter average PM2.5 concentration from 300 μg/m3 to 120 μg/m3, with the most significant decrease occurring after 2014, reflecting the actual impact of China's air pollution control measures. Spatially, the main pollution area shifted from Chongqing to Chengdu and the western basin. The sources of PM2.5 at the eastern and western margins of the basin have remained stable and have been dominated by local emissions for many years, while the sources of PM2.5 in the central part of the basin have evolved from a multiregional co-influenced source during the early period to a high proportion of local emissions; except for boundary condition sources, residential sources were the main PM2.5 sources in the basin (approximately 29.70 %), followed by industrial sources (approximately 14.11 %). Industrial sources exhibited higher contributions in Chengdu and Chongqing and gradually stabilized with residential sources over the years, while residential sources dominated in the eastern and western parts of the basin and exhibited a declining trend. Meteorological conditions exacerbated pollution in the whole basin from 2008 to 2014, especially in the west (21–40 μg/m3). The eastern basin and Chongqing exhibited more years with alleviated meteorological pollution, including a 40+ μg/m3 decrease in Chongqing from 2002 to 2005. Reduced anthropogenic emissions alleviated annual pollution levels, with a greater reduction (> −20 μg/m3) after 2011 due to pollution control measures.

New insights from seasonal and weekly evolutions of aerosol absorption properties and their association with PM2.5 and NO2 concentrations at a central Mediterranean site

Aerosol absorption parameters were investigated in this study using the measurements performed by an aethalometer during a monitoring campaign from December 2021 to July 2022 in a suburban area in south-eastern Italy. The aerosol absorption coefficient was compared to both PM2.5 and NO2 concentrations from two nearby stations (a rural and an urban site). Different seasonal evolutions were identified for these extensive parameters, even if they also showed a common feature because all these parameters presented a higher variability in winter due to the highly variable meteorological conditions during that season. Intensive parameters like the aerosol absorption Ångström exponent and the PM2.5/PM10 ratio were also investigated showing the effects of wood burning from domestic heating and of vehicular traffic, especially both in the urban and in the suburban site. Then, the weekly evolution of both selected extensive and intensive parameters was also analyzed to identify potential impacts due to the weekly cycle of human activities. Most of the selected parameters presented a significant increase starting from Tuesday to Friday and then they generally decreased during the weekends due to the relevant reduction of the human activities. The selection of different types of monitoring sites (urban, suburban, and rural) and temporal scales (seasonal and weekly) has been proved for a proper characterization of the aerosol absorption properties at the monitored area due to its geographical location at the center of the Mediterranean area.

Impacts of emissions and meteorological conditions in three different phases of aerosol pollution during 2013–2022 in Anhui, China

The PM2.5 concentrations in Anhui, which links the Yangtze River Delta region, China's fastest growing economy area, with the Beijing-Tianjin-Hebei (BTH) region, China's most polluted region, are influenced not only by emissions, but also by variation of meteorological conditions. A comprehensive understanding of the relative impacts of meteorology and emissions on heavy pollution in Anhui at three phases (i.e., phase1: from 2013 to 2017; phase2: from 2018 to 2020; phase 3: from 2021 to 2022) from 2013 to 2022, which can provide suggestions for pollution prevention and control in the future. The decrease in pollutant concentrations from 2013 to 2022 is mainly attributed to the continued reduction in emissions, while the year-to-year fluctuations in pollutant concentrations are largely influenced by meteorological conditions. Although emissions are decreasing, the proportions of residential biofuel combustion and cement are increasing. In addition to the effects of prevailing northeasterly and northwesterly winds (i.e., Type1 and Type2), there is also concern about the influences of static weather and neighboring regional transport (i.e., Type5 and Type6), especially in 2016. The contribution of emissions is greater in phase 2 and phase 3, with a 17 % increase compared to phase 1. Overall, approximately 57 % of explosive growth in PM2.5 concentration during the cumulative stage (CS) can be regarded as the feedback effect of the deteriorating meteorological conditions. Therefore, statistical analyses show that limiting PM2.5 concentrations below about 73 μg m−3 would weaken the feedback effects, which in turn would avoid most of the explosive growth processes in the CS of the 60 heavy pollution processes, which can provide a reference for the government to set a target for sustained emission reduction.

Pollution risk and life insurance decisions: Microgeographic evidence from the United Kingdom.

Recent research documents that exposure to air pollution can trigger various behavioral reactions. This article presents novel empirical evidence on the causal effect of pollution risk on life insurance decisions. We create a unique dataset by linking microgeographic air quality information to the confidential UK Wealth and Assets Survey. We identify an inverse N-shape relationship between pollution risk and life insurance adoption by exploiting the orthogonal variations in meteorological conditions. Over a given range above a threshold of exposure, rising pollution is associated with rising demand for life insurance, whereas at lower than the threshold levels of pollution, higher exposure risk reduces demand for insurance. Our findings indicate-for the first time-a nonlinear relationship between local pollution risk and life insurance demand.

Causes Investigation of PM2.5 and O3 Complex Pollution in a Typical Coastal City in the Bohai Bay Region of China in Autumn: Based on One-Month Continuous Intensive Observation and Model Simulation

In order to investigate the causes of complex air pollution in coastal cities in the Bohai Rim of China, a one-month intensive field observation combined with model simulation was carried out in a typical city named Dongying in September 2022. The results showed that two PM2.5 and O3 complex pollution episodes occurred in Dongying in the observation period, with the primary pollutant being O3. Atmospheric photochemical reactions occurring under unfavorable meteorological conditions led to the production of O3 while at the same time facilitating the generation of nitrate, sulfate, and other secondary components of PM2.5 by enhancing the atmospheric oxidizing capacity, which promoted the formation of complex air pollution. It was worth noting that in the context of high pollutants emission, the occurrence of complex air pollution was more sensitive to the variation in meteorological conditions than to the change in pollutants emission. To continuously improve air quality and protect human health in Dongying, it is recommended that an effective regional joint air pollution prevention and control mechanism with neighboring cities should be established in the premise of effective local pollutants reduction, and special attention should be paid to the adverse effect of the air mass transportation from Bohai Bay.

Techno-economic optimization of standalone photovoltaic-wind turbine-battery energy storage system hybrid energy system considering the degradation of the components

The increasing demand for renewable energy sources has led to the development of HRES that combine multiple sources of renewable energy to provide a reliable and efficient energy supply. However, renewable energy sources typically come with higher costs than conventional energy production methods. Hence, the optimal sizing of HRES is essential to prevent the oversizing of the system while ensuring a reliable supply of load demand, accommodating variations in meteorological conditions and changes in power demand. Numerous research papers addressing the optimal sizing problem are available in the literature. However, none of the mentioned studies take into account the power degradation of all renewable sources. The degradation of renewable sources, such as PV and WT, can have a significant impact on the performance of HRES. The degradation of PV panels can result in reduced efficiency and output, leading to a decrease in the overall energy production of the HRES. Similarly, WT may experience wear and tear over time, leading to reduced performance and output. The reduction in the output of renewables in the standalone system causes an increase in the loss of power supply probability (LPSP). This paper analyzes the optimal sizing of the HRES, considering the degradation of the sources. Further, analysis has also been carried out based on the number of BESS to be added to HRES to meet the derived constraints and their impact on the cost of energy as well.

Division of the tropical savanna fire season into early and late dry season burning using MODIS active fires

Tropical savannas and grasslands are the most frequently burned biome in the world, and fire has an important role in sustaining ecosystem processes. Modern management of fires in savannas has roots in traditions stretching back centuries, and nowadays earth observation data is incorporated extensively in fire management practices. In tropical savannas in particular strongly seasonal monsoonal climates allow relatively low severity prescribed burning in the early part of the dry season (EDS) with the goal of preventing more destructive late dry season (LDS) fires. In many regional contexts it is common that a specific, fixed date is used officially to indicate when the window of safe burning has expired and the EDS transitions to the LDS, based on the experience of local or regional fire management authorities. This approach, while practical, neglects inter-annual variability in meteorological conditions and timing of onset of more dangerous fire weather. In this study, we pro-pose a remote sensing-based method for determining when this EDS window expires for five savanna-dominated continental-scale regions. By taking ad- vantage of the fact that conditions allowing night-time burning occur later in the dry season, we use day and night-time active fire detections from the MODerate Resolution Imaging Spectroradiometer (MODIS) instruments to set a flexible date of transition between the EDS and LDS. The vast majority of tropical savannas have very variable (std. dev. ≈ 20–40 days) transition dates, though this is somewhat modulated by fire frequency. Fuel connectivity rather than fuel condition appears to be a strong driving factor behind this variability. We find that especially national parks and protected areas have a high proportion of potentially more severe burning in the LDS, though areas with well-established EDS burning programmes are reducing this impact.

Exploring two-decadal risk variability of drought-flood abrupt alternation in a high-plateau basin

The escalating intensity and frequency of drought-flood abrupt alternation (DFAA) events, intensified by climate change, pose a significant threat to both the eco-environment and human life. Despite their evident impacts, the risk associated with DFAA events has received limited attention. In this study, we present a comprehensive risk assessment model incorporating multifold natural and anthropogenic indicators to analyze the spatiotemporal variations of DFAA risk. Utilizing a DFAA index (DFAAI) based on river flow data from seven monitoring stations spanning 1999 to 2019, we investigate DFAA events in the Dianchi Basin, the largest lake in the Yunnan-Guizhou Plateau, China. Our analysis reveals that 56% of total DFAA events occurred between June and August, with drought-to-flood (DTF) events accounting for 62.5% of DFAA occurrences in the Dianchi Basin. Notably, the intensity and frequency of DFAA events exhibit significant spatial variations across the basin. The hazard, vulnerability, exposure, and disaster defense indices further exhibit substantial spatial discrepancies, with hazard indicators being the most influential, notably the DFAA rate accounting for 72.4% of the overall impact. Spatial correlations in DFAA risk among monitoring stations were found to be low, primarily influenced by variations in meteorological conditions, underlying surface characteristics, and human activities. The findings of this study can offer valuable insights for effective water resource management in an ever-changing environment.

The skill of statistical downscaling in future climate with high-resolution climate models as pseudo-reality

Study regionBelgium Study focusWe assessed statistical downscaling (21 methods) skill and assumptions for seven extreme rainfall indicators in three Belgian areas. The ensemble consists of (i) perturbation methods with four members, (ii) quantile mapping with ten members, and (iii) machine learning with seven members. The methods were evaluated by a perfect predictor experiment in which the outputs of a high-resolution climate model are considered pseudo-observations. The ALARO-0 model with 4 km spatial resolution was considered the high-resolution climate model. The high-resolution outputs were compared with the downscaled results of three future scenarios of the 50 km resolution ALARO-0 model. New hydrological insightsThe methods’ skill depends on the area. The skill is higher for the Belgian coastal area compared to the inland and hilly areas. Methods which are based on stationary assumptions have a good skill for the coastal area but show a lower skill for the other areas where the temporal variability in meteorological conditions is stronger. The higher this temporal variability, the more prone are the stationary assumptions to deteriorate in time under different climate forcing conditions. The quantile-based perturbation methods are overall the most robust. An ensemble approach is advised, where different downscaling methods are applied and the uncertainty in the downscaling taken into account, especially when a pseudo-future evaluation, as done in this study, is not possible.

Analysis of the Influence of Variable Meteorological Conditions on the Performance of the EV Battery and on the Driving Range

The influence of variable weather conditions on the performance of the battery that powers electric vehicles (EV) was studied and analyzed. This paper also deals with the effects that changes in the performance of the battery have on the driving range of the vehicle. An algorithm to evaluate the influence of temperature on the behavior of the battery and on the real driving range of electric vehicles was developed. Our theoretical approach was assessed in experimental tests run under operating conditions that reproduce real situations. A correction factor was obtained to match theoretical and experimental values with an accuracy higher than 98%. A linear relation between driving range and ambient temperature was observed from a simulation process, with a high regression coefficient. The relation shows that the driving range increases with ambient temperature. The ratio of the estimated driving range from the simulation process and the standard value for a reference temperature of 25 °C was obtained. The ratio shows that the global driving range can be increased by up to 29% in high temperatures associated with the summer season, while for very low temperatures, near −30 °C, the global driving range is reduced by 20%. The comparative analysis of the driving range for different temperatures shows that there is a reduction of about 18% for the low range of ambient temperatures, between −15 °C and 5 °C, while for medium temperatures, between 5 °C and 25 °C, the reduction in the driving range is only 4.6%. Finally, tests demonstrated that with a reduction in high temperatures from 25 °C to 35 °C, the driving range only reduced by about 0.4%. For higher temperatures, around 50 °C, the longest driving distance can be achieved, with a higher accuracy.

Transport matrix of PM2.5 in Beijing-Tianjin-Hebei and Yangtze River Delta regions: Assessing the contributions from emission reduction and meteorological conditions

Both meteorological changes and emission control have significant impacts on PM2.5 concentration and regional transport contribution, where it is important to clarify the relationship among them for air pollution control. In this paper, the WRF-CAMx coupled with a set of simulation scenarios was employed for decomposing the contributions of meteorological conditions, emission, and regional transport considering transport matrix to PM2.5 concentration in the Beijing-Tianjin-Hebei (BTH) and Yangtze River Delta (YRD) regions during 2013–2017. The results show that the local contributions occupied the primary position in PM2.5 concentrations in the BTH and YRD regions, reaching 57.9% and 58.1% in 2017, respectively. In terms of regional transports, they were mainly from Shandong Peninsula (SP) and Fen Nutrient Plain (FNP) for BTH, while Central triangle Region (CTR) and SP for YRD. Analysis of typical representative months suggested that in January, the concentration of local contribution reached peak. In addition, the results also show that when the intensity of emission reduction increased, the proportion of local contribution increased significantly while the local absolute contribution concentration decreased correspondingly. The meteorological conditions had little impact on transport matrix dominated by local contribution, whereas, the more favorable the meteorological diffusion conditions, the lower the contribution ratio of the out-of-region transport and the lower the contribution concentration. Moreover, the variation of meteorological conditions was more significant to the transport contribution outside the region. These findings can be helpful for formulating reasonable strategies of regional joint prevention and control and providing a valuable preference for other areas of China.

Research on Photovoltaic Power Prediction Based on BP Neural Network Algorithm

The output power of photovoltaic (PV) power plant is influenced by the fluctuation of meteorological conditions, which, coupled with the intermittent nature of the power output, has raised concerns about the stability of power grid. According to the variations in meteorological conditions, PV power prediction technology can predict the PV output power in advance to decrease the influence of PV power generation on the stable operation of the power grid. In this paper, the PV power prediction model is built on Matlab simulation software with a BP neural network algorithm. After that, the power prediction calculation of a PV power station is carried out under the operation conditions of sunny and cloudy days. The results show that, in the periods of prediction, the power curve of the predicted value is close to the power curve of the real value, which demonstrates the great accuracy of the BP prediction model established in this paper.

Aerosol Physical–Optical Properties under Different Stages of Continuous Wet Weather over the Guangdong–Hong Kong–Macao Greater Bay Area, China

The spatiotemporal distributions and physical–optical properties of aerosols are of great scientific significance for the study of climate change and atmospheric environment. What are the characteristics of aerosols in constant high humidity? Continuous wet weather (CWW) is a special weather phenomenon that occurs frequently during the late winter and early spring in South China. In this study, the CALIPSO satellite data and the ERA5 and MERRA-2 reanalysis data are used to analyze the aerosol optical properties of a total of 68 CWW processes from 2012 to 2021 in the Guangdong–Hong Kong–Macau Greater Bay Area (GBA). We attempt to explore the variations in meteorological conditions and physical–optical properties of aerosols during the before-stage, wet-stage, and after-stage under different humidity levels. The results show that the prevailing wind direction is northeasterly and that the temperature and humidity are lower under the influence of cold high pressure in the before-stage. Moreover, the high aerosol optical depth (AOD) mainly results from regional transport. During the wet-stage, clean ocean airflow causes AOD to remain at a low level, whereas temperature and humidity increase significantly. The wet-stage ends with coldness when it is controlled by cold high pressure again. The atmospheric circulation in the after-stage is similar to that in the before-stage. However, a remarkable feature is that there is a temperature and humidity inversion layer, which results in a significant increase in AOD. This study reveals the physical–optical properties of aerosols during the three stages and the influence mechanism of meteorological factors on aerosols, which can provide a scientific basis for the study of CWW in the future.

Spatially heterogeneous relationships of PM2.5 concentrations with natural and land use factors in the Niger River Watershed, West Africa

Identifying the main factors influencing PM2.5 concentrations is critical for the control and management of elevated atmospheric pollution. Although the impact of natural and land use factors on PM2.5 concentration has previously been quantified, in this paper we show that multiple environmental factors exhibit significant spatial heterogeneity at a range of scales. A more comprehensive analysis of PM2.5 variations require the potential effects of seasonal variations in meteorological conditions, interactions between natural and land use factors, and possible multi-scale spatial effects to be accounted for. Based on the global remote sensing inversion dataset, the aim of this study is to elucidate the spatially heterogeneous relationships of PM2.5 concentrations with natural and land use factors, and their multi–scale effects. This is achieved through combining geographical detector (GD), random forest (RF) and multiscale geographically weighted regression modelling (MGWR) to reveal the underlying mechanisms of spatial variation of PM2.5 concentrations in the Niger River Watershed (NRW). The results show that natural factors, represented by meteorological and topography variables, are most prominent. Human activities, represented by land use, have significant indirect effects through interactions with meteorological factors. Elevation (ELE) and annual minimum temperature (TMIN) emerge as the most important interaction centers among natural factors. In addition, the interaction between natural and land use factors are crucial in considering PM2.5 transmission and dispersion in the NRW. The topography and meteorological factors underlie the basic spatial pattern PM2.5 concentrations in the NRW, among which seasonal meteorological parameters have a more prominent impact on PM2.5 concentration. Both natural and land use factors exhibit strong non–stationarity in respect of spatial variations in PM2.5, and the effect of environmental factors in general on PM2.5 is scale–dependent. Transport and dispersion are important constraints in terms of aerosol loading regionally. The study suggests that coupling processes between meteorological conditions, land use and PM2.5 concentrations, based on the scale effects of each factor, should be accounted for in developing aerosol pollution regulations and cooperative management strategies in the region.