Influence Of Meteorological Conditions Research Articles

Impact of the 2020 COVID-19 lockdown on NO2 and PM10 concentrations in Berlin, Germany

In March 2020, the World Health Organization declared a pandemic due to the rapid and worldwide spread of the SARS-CoV-2 virus. To prevent spread of the infection social contact restrictions were enacted worldwide, which suggest a significant effect on the anthropogenic emission of gaseous and particulate pollutants in urban areas. To account for the influence of meteorological conditions on airborne pollutant concentrations, we used a Random Forest machine learning technique for predicting business as usual (BAU) pollutant concentrations of NO2 and PM10 at five observation sites in the city of Berlin, Germany, during the 2020 COVID-19 lockdown periods. The predictor variables were based on meteorological and traffic data from the period of 2017–2019. The differences between BAU and observed concentrations were used to quantify lockdown-related effects on average pollutant concentrations as well as spatial variation between individual observation sites. The comparison between predicted and observed concentrations documented good overall model performance for different evaluation periods, but better performance for NO2 (R2 = 0.72) than PM10 concentrations (R2 = 0.35). The average decrease of NO2 was 21.9% in the spring lockdown and 22.3% in the winter lockdown in 2020. PM10 concentrations showed a smaller decrease, with an average of 12.8% in the spring as well as the winter lockdown. The model results were found sensitive to depict local variation of pollutant reductions at the different sites that were mainly related to locally varying modifications in traffic intensity.

Effects of different types of heat wave days on ozone pollution over Beijing-Tianjin-Hebei and its future projection.

There are concerns that in the 21st century, global warming will lead to more frequent heat wave days (HWDs), which could amplify ozone pollution (OP). However, a recent study projected that future atmospheric circulation variations may benefit OP control in Beijing-Tianjin-Hebei (BTH). To investigate the possible reasons for this contradiction, this paper discussed the ozone amplification capacity between different HWD types and their future projections based on observations and Community Earth System Model Large Ensemble Simulations (CESM-LENS). Composite analysis shows that not all HWDs amplify the OP in BTH. The main factor determining whether HWDs aggravate OP is the accompanying circulation anomalies rather than the intensity of the HWDs. The HWDs that aggravate ozone pollution are usually accompanied by stable saddle-like circulation anomalies and atmospheric blocking (blocking HWDs), which weaken the meridional temperature gradient and strengthen atmospheric stability. In contrast, HWDs with wave-train circulation anomalies have a limited ability to exacerbate OP in BTH due to their weak atmospheric stability. We introduce the Ozone Weather Index (OWI) to discern the influence of meteorological conditions on OP and overcome the lack of ozone concentration data in CESM-LENS under RCP 8.5 scenario. The OWI shows a significant downward trend in the 21st century, which indicates that the summer mean atmospheric circulation variations are beneficial for OP control in BTH. However, the frequency of blocking HWDs, which could amplify OP, will increase significantly in the 21st century. By the end of the 21st century, the frequency of blocking HWDs will be three times more than that of the end of 20th century. These findings inform policymakers that it is imperative to consider the mean climate state and the risks associated with extreme events when formulating future ozone pollution control policies.

Influence of Meteorological Conditions on Nosema in Shenyang City from 2018 to 2020

Influence of Meteorological Conditions on Nosema in Shenyang City from 2018 to 2020

The effectiveness of traffic and production restrictions on urban air quality: A rare opportunity for investigation

ABSTRACT Traffic and production restrictions are two important emergency measures for controlling urban air pollution. The lockdown policies implemented during the COVID-19 pandemic period are nearly equivalent to the policies of traffic and production restriction, which provides a rare opportunity to quantitatively evaluate the effectiveness of these emergency measures. Taking Wuhan, China as the study area, this paper firstly verified the changes in six air pollutants and analyzed their change rules in different lockdown periods using statistical methods. Then the structural breakpoints in air pollutants were detected via regression discontinuity design model. To comprehensively understand the effects of restrictions on air pollution, the influences of meteorological conditions on air pollution were also investigated. The results illustrated that the concentrations of PM2.5, PM10 and NO2 decreased significantly during lockdown period. By comparing with the RDD coefficients of PM2.5 (−34.46), PM10 (−37.11) and NO2 (−19.15), the lockdown had little effect on CO (−0.32). The traffic and production restrictions had no apparent effects on SO2. Although O3 showed an increasing trend, the increase was not limited to the lockdown period, meaning that the traffic and production restrictions had less effect on the increasing trend of O3 concentration. Moreover, the structural breakpoints were verified in four air pollutants (PM2.5, PM10, NO2, and CO), and the structural breakpoints were caused by lockdown instead of the Spring Festival. The results also indicated that the meteorological conditions were not the main reasons for the changes in air pollutants during the lockdown period. This paper reveals how the traffic and production restrictions affect urban air pollution and provides a strong implementation basis for the air pollution control policy. Implications: The traffic and production restrictions are two important emergency measures for controlling heavy urban air pollution. However, these two measures have never been implemented in a large area like a city for a long enough period, so the effectiveness of these two measures has never been estimated quantitatively at a city level. The lockdown policies implemented during the COVID-19 pandemic are nearly equivalent to the policies of traffic and production restriction, which provides a rare opportunity to quantitatively evaluate the effectiveness of these emergency measures. Thus, this study measured the effectiveness of production and traffic restrictions on different air pollutants. This study provides the following implications: (1) the dominant factors for air pollution changes during the lockdown are traffic and production restriction instead of meteorological conditions; (2) the production and traffic restriction policies are effective for reducing concentrations of PM2.5, PM10 and NO2, while having less effect on O3 and CO concentrations; (3) the sharp changes in air pollutants in 2020 are unlikely to be caused by the Spring Festival. These findings are crucial for making more comprehensive policies for protecting urban air quality.

The Chinese Spring Festival Impact on Air Quality in China: A Critical Review

It is known that the sharp change of air pollutants affects air quality. Chinese Spring Festival is the most important holiday for Chinese people, and the celebration of the holiday with fireworks and the movement of people all around the country results in significant change in multiple air pollutant emissions of various sources. As many cities and rural areas suffer from the air pollution caused by firework displays and more residential fuel consumption, there is an urgency to examine the impact of the Chinese Spring Festival on air quality. Hence, this paper firstly gives an overall insight into the holiday’s impact on ambient and household air quality in China, both in urban and rural areas. The main findings of this study are: (1) The firework displays affect the air quality of urban and rural atmosphere and household air; (2) the reduction in anthropogenic emissions improves the air quality during the Chinese Spring Festival; (3) the household air in urban areas was affected most by firework burning, while the household air in rural homes was affected most by fuel consumption; and (4) the short-term health impact of air pollution during the holidays also need more concern. Although there have been many publications focused on the holiday’s impact on ambient and household air quality, most of them focused on the measurement of pollutant concentration, while studies on the formation mechanism of air pollution, the influence of meteorological conditions, and the health outcome under the effect of the Chinese Spring Festival are rare. In the future, studies focused on these processes are welcomed.

Biological features of the development of early potato varieties in the Murmansk region

Potato (Solanum tuberosum L.) is a fairly popular agricultural crop in Russia, the tubers of which are the main food of the population. For the North, it is important to replenish the assortment of early potato varieties adapted to growing in extreme conditions. The aim of this research was to study the development of plants of early and mid-early potato varieties in the Murmansk region.Methods. The article presents the results of studying the developmental biology and productivity elements of 63 early and mid-early varieties from the of VIR world potato collection. The Khibinskiy ranniy variety is used as a standard. The study was conducted on the territory of the Polar Experimental Station of Apatity. The soil of the experimental plot is sandy loam, highly cultivated, the organic matter content is 8,7%, pH 5,2; Р2О5 – 103 mg/100 g, К2О – 39,2 mg/100 g of soil. The total area of the plot was 286,5 m2 , the counting area was 5 m2, the planting scheme was 70x30 cm, the experiment was repeated three times, the distribution of varieties was randomized.Results. Phenological observations revealed varieties with the shortest interphase periods: Ognivo, Sundinsky ranniy, Uralsky ranniy, Khibinskiy ranniy, Amazone, Aster, Lady Claire. Varieties Daryonka, Lyubava, Dolphin formed berries within three years.Conclusion. Concorde, Corine и Karatop exceeded the standart at 50, 60, 75 days from planting in the years of study, which shows the ability of the cultivars for intensive tuberization in the early stages, combined with high stable productivity in the North. The significant influence of meteorological conditions on the development dynamics of potato plants, their yield and on the adaptive ability to local growing conditions has been identified. There singled out the cultivars that are characterized by consistently high rates of the dynamics of tuber accumulation in the early stages, possessing a high adaptive ability, which can be used in breeding for early maturity and productivity in the North regions.

Characteristics of PM2.5 and PM10 Spatio-Temporal Distribution and Influencing Meteorological Conditions in Beijing

PM2.5 and PM10 in the atmosphere seriously affect human health and air quality, a situation which has aroused widespread concern. In this paper, we analyze the temporal and spatial distribution of PM2.5 and PM10 concentrations from 2016 to 2021 based on real-time monitoring data. In addition, we also explore the influence of meteorological conditions on pollutants. The results show that PM2.5 and PM10 concentrations are similarly distribution in temporal and spatial from 2016 to 2021, and the average concentrations of both show a decreasing trend. The ratio of PM2.5 to PM10 is decreasing, indicating that the proportion of fine particles is declining. PM2.5 and PM10 concentrations are higher in spring and winter, but lower in summer. Spatially, it shows a gradual shift from the characteristic of “high in the south and low in the north” to a uniform homogenization across districts. The spatial distribution of PM2.5 and PM10 mass concentrations is synchronous by applying empirical orthogonal functions (EOF). The first EOF pattern exhibits a consistent characteristic of high in the southeast and low in the northwest. The second pattern EOF reflects the effect of impairing PM2.5 concentrations in the southeast during the winter of 2016–2018. The PM2.5 and PM10 concentrations are significantly negatively correlated with wind speed and precipitation in both spring and winter. On the other hand, from the perspective of the circulation situation, the southeasterly and weak westerly wind in spring produce convergence resulting in higher particulate matter concentrations in the south than in the north in Beijing. The westerly wind is flatter at 700 hPa geopotential height, which is conducive to the formation of stationary weather. The vertical direction of airflow in spring and winter is dominated by convergence and sinking, indicating the weak dispersion ability of the atmosphere. The reason for the accumulation of particulate matter at the surface is investigated, which is beneficial to provide the theoretical basis for air quality management and pollution control in Beijing.

THE INFLUENCE OF WINTER-SPRING TEMPERATURES AND PRECIPITATION ON THE SPRING FLOOD WAVE

The complex influence of meteorological conditions on the first (April) wave of spring-summer flooding of the mountain rivers was studied by the example of 34 mid-size and small rivers of the Altai-Sayan mountain country covering more than 2,000,000 km2 of the Central Asia territory. System-analytical modeling was employed to describe the complex dependence of April river runoff on air temperatures and precipitation for December-April. The impact of factors was quantitatively assessed through solving the inverse mathematical problem and using 1,760 values of river runoff for 1951-2019, meteorological data and carto- graphic information on the relief and landscape structure of river basins calculated in ArcGIS 10.2. Precipitation accumulated in December-March as snow cover had a positive effect on runoff only in case of proximity of winter temperatures to their long-term mean values. At low April temperatures, river runoff increased along with the growth of current precipitation, while at temperature rise it decreased. The established dependencies of the first flood wave on meteorological factors are of great importance for effective preventing adverse effects of spring floods in the mountain regions.

Influence of meteorological conditions and topography on the active space of mountain birds assessed by a wave-based sound propagation model.

The active space is a central bioacoustic concept to understand communication networks and animal behavior. Propagation of biological acoustic signals has often been studied in homogeneous environments using an idealized circular active space representation, but few studies have assessed the variations of the active space due to environment heterogeneities and transmitter position. To study these variations for mountain birds like the rock ptarmigan, we developed a sound propagation model based on the parabolic equation method that accounts for the topography, the ground effects, and the meteorological conditions. The comparison of numerical simulations with measurements performed during an experimental campaign in the French Alps confirms the capacity of the model to accurately predict sound levels. We then use this model to show how mountain conditions affect surface and shape of active spaces, with topography being the most significant factor. Our data reveal that singing during display flights is a good strategy to adopt for a transmitter to expand its active space in such an environment. Overall, our study brings new perspectives to investigate the spatiotemporal dynamics of communication networks.

The Extraordinary Trend of the Spatial Distribution of PM2.5 Concentration and Its Meteorological Causes in Sichuan Basin

Sichuan Basin is an area with some of the most serious PM2.5 pollution, and it is also a key area for joint prevention and control of air pollution in China. Therefore, it is necessary to clarify the temporal and spatial distribution characteristics of PM2.5 concentration in Sichuan Basin (SCB) and study the influence of meteorological conditions. In this study, the spatial disparity of PM2.5 concentration in SCB and its variation trend from 1 December 2015 to 30 November 2019 were analyzed. The results showed that the spatial disparity of SCB was decreasing and distinct variation trends of PM2.5 concentration were observed in different areas. The PM2.5 concentrations declined rapidly in the western and southern basin (most severely polluted areas), decreased at a slower rate in the central and eastern basin, but unexpectedly increased slightly in the northern and northeastern basin. From the perspective of relative spatial anomalies (RAs), the decreasing (increasing) trend of RAs of PM2.5 concentrations in the western and southern (northern and northeastern) parts of SCB were also prominent. The reduction in spatial disparity and the regionally extraordinary increasing trend could be partly explained by the variations in synoptic circulations. Specifically, the reasons for the decrease in wintertime spatial disparity and the increase in RAs in the northern basin were the reduction in synoptic pattern Type 2 (weak high-pressure system and uniform pressure fields) and Type 3 (high-pressure system to the north) and the growth of Type 6 (weak low-pressure system with high-pressure system to the north). In spring, the reasons were the reduction in Type 1 (weak low-pressure system) and Type 5 (weak low-pressure system to the southwest) and the growth of Type 2. The reduction in Type 2 and the growth in Type 4 (weak high-pressure system to the east) could explain the variation in PM2.5 distribution in autumn. This study showed the importance of implementing more precise and effective emission control measures, especially in relatively cleaner areas, in which the impacts of meteorological conditions might cause fluctuation (even rebounding) in the PM2.5 concentration.

Ozone pollution during the COVID-19 lockdown in the spring of 2020 over Europe, analysed from satellite observations, in situ measurements, and models

Abstract. We present a comprehensive study integrating satellite observations of ozone pollution, in situ measurements, and chemistry-transport model simulations for quantifying the role of anthropogenic emission reductions during the COVID-19 lockdown in spring 2020 over Europe. Satellite observations are derived from the IASI+GOME2 (Infrared Atmospheric Sounding Interferometer + Global Ozone Monitoring Experiment 2) multispectral synergism, which provides better sensitivity to near-surface ozone pollution. These observations are mainly analysed in terms of differences between the average on 1–15 April 2020, when the strictest lockdown restrictions took place, and the same period in 2019. They show clear enhancements of near-surface ozone in central Europe and northern Italy, as well as some other hotspots, which are typically characterized by volatile organic compound (VOC)-limited chemical regimes. An overall reduction of ozone is observed elsewhere, where ozone chemistry is limited by the abundance of NOx. The spatial distribution of positive and negative ozone concentration anomalies observed from space is in relatively good quantitative agreement with surface in situ measurements over the continent (a correlation coefficient of 0.55, a root-mean-squared difference of 11 ppb, and the same standard deviation and range of variability). An average difference of ∼ 8 ppb between the two observational datasets is observed, which can partly be explained by the fact the satellite approach retrieves partial columns of ozone with a peak sensitivity above the surface (near 2 km of altitude over land and averaging kernels reaching the middle troposphere over ocean). For assessing the impact of the reduction of anthropogenic emissions during the lockdown, we adjust the satellite and in situ surface observations for subtracting the influence of meteorological conditions in 2020 and 2019. This adjustment is derived from the chemistry-transport model simulations using the meteorological fields of each year and identical emission inventories. Using adjustments adapted for the altitude and sensitivity of each observation, both datasets show consistent estimates of the influence of lockdown emission reduction. They both show lockdown-associated ozone enhancements in hotspots over central Europe and northern Italy, with a reduced amplitude with respect to the total changes observed between the 2 years and an overall reduction elsewhere over Europe and the ocean. Satellite observations additionally provide the ozone anomalies in the regions remote from in situ sensors, an enhancement over the Mediterranean likely associated with maritime traffic emissions, and a marked large-scale reduction of ozone elsewhere over ocean (particularly over the North Sea), in consistency with previous assessments done with ozone sonde measurements in the free troposphere. These observational assessments are compared with model-only estimations, using the CHIMERE chemistry-transport model. Whereas a general qualitative consistency of positive and negative ozone anomalies is observed with respect to observational estimates, significant changes are seen in their amplitudes. Models underestimate the range of variability of the ozone changes by at least a factor 2 with respect to the two observational datasets, both for enhancements and decreases of ozone. Moreover, a significant ozone decrease observed at a large hemispheric scale is not simulated since the modelling domain is the European continent. As simulations only consider the troposphere, the influence from stratospheric ozone is also missing. Sensitivity analyses also show an important role of vertical mixing of atmospheric constituents, which depends on the meteorological fields used in the simulation and significantly modify the amplitude of the changes of ozone pollution during the lockdown.

Climatic and agronomic impacts on sugar beet (Beta vulgaris L.) production

The use of suitable technologies helps crop cultivation under unfavorable and extreme weather conditions obtain the optimum yield by influencing irrigation, fertilization, sowing time, and crop density. The study aimed to determine the impact of adaptive technologies on sugar beet (Beta vulgaris L.) cultivation at the Kazakh Research Institute of Agriculture and Plant Growing, Almaty Region, Kazakhstan. The phenological observations on basic phases of sugar beet growth and development, and plant density were done according to the generally accepted methods. Moreover, the photosynthetic activity of crop productivity was studied through the accumulation of raw and dry biomass (weight method), area determination of the assimilation apparatus (die-cutting method), and the advent of photosynthetic active radiation. The influence of meteorological conditions was particularly noted on plots with moisture deficit. Maintaining such pre-irrigation soil moisture at 60% of LMC (Least Moisture Capacity) requires less watering with large irrigation rates (1020-1260 m3 ha-1) with inter-irrigation periods of 30–37 days. In 2016, three irrigations with the rate of 1220-1260 m3 ha-1 were done. Maximum water consumption occurs from the end of July to the beginning of August. Consumption of spring reserves for soil moisture was 8%-10% higher at late harvesting than at early harvesting. During the crop season with high rainfall distributed uniformly over the vegetation period, spring soil moisture reserves consumption increased and their share in total water consumption increased 12% up to 20%, whereas, during dry seasons it decreased 6% up to 14%. The study noted that for producing the sugar beet yields ranging from 22.6 to 65.2 t ha-1, the NPK should be applied at the rate of nitrogen (32 – 215 kg ha-1), phosphorus (12–68 kg ha-1), and potassium (50–380 kg ha-1), It was also found necessary to apply fertilizers differentially depending on the level of applied technology for the planned beet yield.

The influence of meteorological factors on the yield and grain quality of soybean varieties belonging to different groups of ripeness in the conditions of the Stavropolupland

Soy is a unique leguminous crop, the grain of which is used by the processing industry for the production of protein concentrate, vegetable fat, biofuels, and concentrated feed. The experiments were laid in 2008-2011 and 2017-2019, at the experimental station of the Stavropol State Agrarian University. Soybean varieties were sown: precocious - Lira (St1) and Selecta 101, early-ripening - Duar (St2) and Selecta 201, medium-ripe - Vilana (St3) and Selecta 302. In the conditions of the Stavropol upland, the duration of the growing season from germination to the ripening of soybeans in the precocious group was 106 days, early-ripening - 119, middle-ripening - 130 days. The greatest dependence of the grain harvest was in July, the yield by 71.6% was determined by the influence of meteorological conditions. In August, the yield dependence decreased, but is still very noticeable, the yield by 51.5% was determined by the influence of the amount of precipitation and the amount of active temperatures greater than 10 °C. The yield of soybean grain varied in: precocious varieties from 1.49 to 1.52 t/ha, early–ripening - from 1.70 to 1.78 t/ha, medium-ripened - from 1.64 to 1.77 t/ha. The highest quality grain was obtained by growing the early-ripening Duar variety (St2), the crude protein content was 41.3%, and vegetable fat - 22.3%. When growing this variety, 0.662 t/ha of vegetable protein and 0.357 t/ha of vegetable fat were obtained.

Revealing influence of meteorological conditions and flight factors on delays Using XGBoost

With the increasing demand for air transportation, the negative impact of flight delays has been paid more and more attention, especially in the hubs of large cities. By examining flight delay data and analyzing the main factors affecting flight delays, the causes of flight delays can be found and effectively avoided. In this paper, we collect meteorological data and flight data of New York’s John F. Kennedy International Airport (JFK), Laguardia Airport (LGA), and Newark Liberty International Airport (EWR). By consulting relevant data, we select the factors that may have a strong correlation with flight delays, and we simplify and classify the data. Based on the preliminary analysis of the relationship between a single factor and flight delays, we use XGBoost to predict and analyze flight delays. We find that: (1) the effect of a single feature on flight delays is limited; (2) departure time, carrier, and precipitation have a great influence on flight delays; and (3) the accuracy of the prediction results of the change of delay duration during flight is better than the departure delay and arrival delay. Our research results can help airports combine meteorological conditions and forecasts to arrange flights properly and reduce the rate of flight delays and the losses to airlines and passengers.

Influence of Meteorological Conditions on Artificial Ice Reservoir (Icestupa) Evolution

Since 2014, mountain communities in Ladakh, India have been constructing dozens of Artificial Ice Reservoirs (AIRs) by spraying water through fountain systems every winter. The meltwater from these structures is crucial to meet irrigation water demands during spring. However, there is a large variability associated with this water supply due to the local weather influences at the chosen location. This study compared the ice volume evolution of an AIR built in Ladakh, India with two others built in Guttannen, Switzerland using a surface energy balance model. Model input consisted of meteorological data in conjunction with fountain discharge rate (mass input of an AIR). Model calibration and validation were completed using ice volume and surface area measurements taken from several drone surveys. The model was successful in estimating the observed ice volume evolution with a root mean square error within 18% of the maximum ice volume for all the AIRs. The location in Ladakh had a maximum ice volume four times larger compared to the Guttannen site. However, the corresponding water losses for all the AIRs were more than three-quarters of the total fountain discharge due to high fountain wastewater. Drier and colder locations in relatively cloud-free regions are expected to produce long-lasting AIRs with higher maximum ice volumes. This is a promising result for dry mountain regions, where AIR technology could provide a relatively affordable and sustainable strategy to mitigate climate change induced water stress.

Mangrove Growth Monitoring Based on Camera Visible Images—A Case Study on Typical Mangroves in Guangxi

Mangroves are an important coastal wetland ecosystem, and the high-throughput visible light (RGB) images of the canopy obtained by the ecological meteorological station can provide basic data for quantitative and continuous growth monitoring of mangroves. However, as for the mangroves that are subject to periodic seawater submersion, some key technical issues such as image selection, vegetation segmentation, and index applicability remain unsolved. With the typical mangroves in Beihai, Guangxi, as the object in this study, we used canopy RGB images and tidal data to find out the screening methods for high-quality nontidal submerged images, as well as the vegetation segmentation algorithms and RGB vegetation index applicability, so as to provide technical reference for the use of RGB images to monitor mangrove growth. The results showed that: 1) The critical tide levels can be determined according to the periodic changes of submersion in the mangroves, and critical tidal levels and image brightness can be used to quickly screen high-quality images of mangroves that are not submerged by seawater. 2) Machine learning and NLM filtering are effective strategies to obtain high-precision mangrove segmentation results. The machine learning algorithm has superiority in the segmentation of mangrove vegetation with a segmentation accuracy of higher than 80%, and the nonlocal mean filtering can effectively optimize the segmentation results of various algorithms. 3) The seasonal index VEG and antiseasonal index CIVE can be used as the optimal indices for mangrove growth monitoring, and the compound sine function can better simulate the change trend of various RGB vegetation indices, which is convenient for quickly judging mangrove growth changes. 4) Mangrove RGB vegetation indices are sensitive to meteorological factors and can be used to analyze the influence of meteorological conditions on mangrove growth.

Impact of Meteorological Conditions on PM2.5 in Jiangsu Province from 2001 to 2019

The weather research and forecasting model coupled with chemistry (WRF-Chem) was used to investigate the impact of meteorological conditions on PM2.5 in Jiangsu Province from 2001 to 2019. Under the condition of constant emission sources, the strongest positive and negative anomalies of annual PM2.5 concentration caused by meteorological conditions occurred in 2008 and 2001, respectively. Furthermore, their anomalies respectively accounted for 10.5% and -14.3% relative to the long-time averaged annual PM2.5 concentration, indicating that meteorological conditions are an important factor causing the interannual variation in PM2.5 concentration in Jiangsu Province. The empirical orthogonal function decomposition results show that the influence of meteorological conditions on the spatial distribution of PM2.5 concentration in Jiangsu Province is consistent under this mode. Additionally, the boundary layer height, temperature, relative humidity, wind speed, and precipitation all have significant negative correlations with the PM2.5 concentration in Jiangsu Province. Meanwhile, the linear regression equation constructed by the above meteorological factors can characterize the relationship between PM2.5 concentration and meteorological conditions well. Moreover, the correlation between the fitting value and the simulated value was 0.73, which was statistically significant at a 99% confidence level according to a student's t test.

Unexpected Increases of Severe Haze Pollution During the Post COVID‐19 Period: Effects of Emissions, Meteorology, and Secondary Production

AbstractUnexpectedly frequent severe haze episodes were observed in Beijing during February–March in 2021 after two phases of clean air action plan (2013–2020), yet the causes remained unclear. Here, we conducted real‐time fine particle (PM2.5) composition measurements during January–March in 2021 using a time‐of‐flight aerosol chemical speciation monitor and an aethalometer and compared with those during the coronavirus disease (COVID‐19) period in 2020. Our results showed ubiquitously elevated concentrations of chloride, black carbon (BC), and primary organic aerosol (POA) in 2021, suggesting increased primary emissions during the post‐COVID‐19 period. By using the machine learning‐based random forest (RF) algorithm, we found largely different responses of aerosol changes to meteorology in different months. After decoupling the effects of meteorology, the PM2.5 changes from 2020 to 2021 were reduced from −35.6% to −29.0% in January, −24.1% to −4.5% in February, and +92.6% to +34.2% in March, respectively. Our results demonstrate the dominant roles of stagnant meteorology and secondary production in the formation of severe haze episodes in March 2021. In particular, we found that the compositions of observed and deweathered PM2.5 were fairly similar between 2020 and 2021, and the ratios of secondary OA to secondary inorganic aerosols were close. Our study indicates that decoupling the influence of meteorological conditions is of great importance for better evaluation of mitigating strategies of air pollution due to the large impact of meteorology on the changes in PM2.5 species particularly in a short period.

Impact of aerosols on warm clouds over the Sichuan Basin, China in winter based on the MERRA-2 reanalysis dataset

The impacts of aerosols on low warm clouds over the Sichuan Basin are still not well understood. We used the long-term Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) dataset to examine the relationships between aerosols and warm clouds in winter and explored the influence of meteorological conditions and different types of aerosols. There was a positive relationship between the aerosol optical depth and the cloud effective radius, cloud fraction and cloud optical thickness. However, the cloud top pressure tended to decrease with increasing aerosol optical depth. The influence of aerosols on warm clouds varied with the meteorological conditions. An analysis of the effects of thermal conditions, the vertical velocity and the relative humidity showed that stable atmospheric conditions, strong updrafts and a higher relative humidity increased the cloud fraction. Unstable atmospheric conditions and a high relative humidity also facilitated the formation of higher and thicker clouds. High sulfate aerosols were associated with moist air, a greater cloud fraction, a greater cloud optical thickness and a smaller cloud top pressure. Conversely, there was a smaller cloud fraction and cloud optical thickness and a greater cloud top pressure with drier air when there was a higher load of dust and black carbon aerosols.

Weather patterns and occurrence of epileptic seizures

BackgroundThe results of various studies that have so far evaluated the influence of meteorological conditions on seizures are often divergent. No studies have been performed so far that evaluate the possible correlation between the occurrence of seizure-related events, surface and upper level atmospheric conditions and biometeorological forecasts. The aim of this study is to investigate those interactions.MethodsRecords of “Sveti Duh” University hospital, Zagreb, Croatia between January 2016 and May 2020 were analysed in order to determine number of patients per day who were assessed through Emergency room because of a recent epileptic seizure. The dates were assessed for surface and upper level atmospheric conditions and biometeorological forecasts. Analyses of these factors were performed for two consecutive days preceding that day, the day of seizure, as well as for the following day. Data was analysed using chi-squared test, Mann-Whitney U-test or Kruskal-Wallis test (with Conover’s test for post-hoc analyses), depending on variable type. Additionaly, logistic regression was used to study effects of variables on seizure occurrence. Statistical significance was set to p < 0.05.ResultsResults showed significant difference between the numbers of patients with seizure depending on upper level type on the following day. We also observed fewer daily patients with seizures when the synoptic situation on the following day was high pressure field then when it was low pressure or non-gradient pressure field (observed just during the colder part of the year), which was supported in our regression analyses. A greater frequency of seizures was observed if the biometerological prognosis was deemed unfavourable on the following day. Furthermore, our results showed significantly larger proportion of days with seizure patients in warmer, than in colder days of the year.ConclusionsAll of the analyzed weather-related parameters seem to be associated with daily numbers of seizures on the previous day. The reason behind this phenomenon is uncertain; however, the results indicate that weather patterns have a certain influence on patients with epilepsy, but are not yet fully understood or adequately appreciated.