Empirical Analysis of Autonomous Vehicle's LiDAR Detection Performance Degradation for Actual Road Driving in Rain and Fog.

Light detection and ranging (LiDAR) is widely used in autonomous vehicles to obtain precise 3D information about surrounding road environments. However, under bad weather conditions, such as rain, snow, and fog, LiDAR-detection performance is reduced. This effect has hardly been verified in actual road environments. In this study, tests were conducted with different precipitation levels (10, 20, 30, and 40 mm/h) and fog visibilities (50, 100, and 150 m) on actual roads. Square test objects (60 × 60 cm2) made of retroreflective film, aluminum, steel, black sheet, and plastic, commonly used in Korean road traffic signs, were investigated. Number of point clouds (NPC) and intensity (reflection value of points) were selected as LiDAR performance indicators. These indicators decreased with deteriorating weather in order of light rain (10-20 mm/h), weak fog (<150 m), intense rain (30-40 mm/h), and thick fog (≤50 m). Retroreflective film preserved at least 74% of the NPC under clear conditions with intense rain (30-40 mm/h) and thick fog (<50 m). Aluminum and steel showed non-observation for distances of 20-30 m under these conditions. ANOVA and post hoc tests suggested that these performance reductions were statistically significant. Such empirical tests should clarify the LiDAR performance degradation.

The impact of weather condition and social activity on COVID-19 transmission in the United States.

The coronavirus disease 2019 (COVID-19) has been first reported in December 2019 and rapidly spread worldwide. As other severe acute respiratory syndromes, it is a widely discussed topic whether seasonality affects the COVID-19 infection spreading. This study presents two different approaches to analyse the impact of social activity factors and weather variables on daily COVID-19 cases at county level over the Continental U.S. (CONUS). The first one is a traditional statistical method, i.e., Pearson correlation coefficient, whereas the second one is a machine learning algorithm, i.e., random forest regression model. The Pearson correlation is analysed to roughly test the relationship between COVID-19 cases and the weather variables or the social activity factor (i.e. social distance index). The random forest regression model investigates the feasibility of estimating the number of county-level daily confirmed COVID-19 cases by using different combinations of eight factors (county population, county population density, county social distance index, air temperature, specific humidity, shortwave radiation, precipitation, and wind speed). Results show that the number of daily confirmed COVID-19 cases is weakly correlated with the social distance index, air temperature and specific humidity through the Pearson correlation method. The random forest model shows that the estimation of COVID-19 cases is more accurate with adding weather variables as input data. Specifically, the most important factors for estimating daily COVID-19 cases are the population and population density, followed by the social distance index and the five weather variables, with temperature and specific humidity being more critical than shortwave radiation, wind speed, and precipitation. The validation process shows that the general values of correlation coefficients between the daily COVID-19 cases estimated by the random forest model and the observed ones are around 0.85.

Long-term effects of weather condition and air pollution on acute hemorrhagic conjunctivitis in China: A nationalwide surveillance study in China.

BACKGROUND: Global climate change could have potential impact on enterovirus (EV)-induced infectious diseases. However, the environmental factors promoting acute hemorrhagic conjunctivitis (AHC) circulation remain inconclusive. This study aimed to quantify the relationship between the environment and AHC. METHODS: We retrieved the monthly counts and incidence of AHC, meteorological variables and air quality in mainland China between 2013 and 2018. Exposure risks were evaluated by multivariate distributed lag nonlinear models. RESULTS: A total of 219,599 AHC cases were reported in 31 provinces of China, predominantly in southern and central China, seasonally increased in summer. AHC incidence increased by 7% between 2013 and 2018, from 2.6873 to 2.7570 per 100,000 people. A moderate positive correlation was seen between AHC and monthly mean temperature, relative humidity (RH) and precipitation. Each unit increment was associated with a relative risk for AHC of 1.058 at 17°-32 °C at lag 0 months, 1.017 at 65-71% RH at lag 1.4 months, and 1.039 at 400-569 mm at lag 2.4 months. By contrast, a negative correlation was seen between monthly ambient NO2 and AHC. CONCLUSION: Long-term exposure to higher mean temperature, RH and precipitation were associated with an increased risk of AHC. The general public, especially susceptible populations, should pay close attention to weather changes and take protective measures in advance to any AHC outbreak as the above situations occur.

The role of weather conditions in COVID-19 transmission: A study of a global panel of 1236 regions.

It is believed that weather conditions such as temperature and humidity have effects on COVID-19 transmission. However, these effects are not clear due to the limited observations and difficulties in separating impact of social distancing. COVID-19 data and social-economic features of 1236 regions in the world (1112 regions at the provincial level and 124 countries with the small land area) were collected. Large-scale satellite data was combined with these data with a regression analysis model to explore the effects of temperature and relative humidity on COVID-19 spreading, as well as the possible transmission risk by seasonal cycles. The result shows that temperature and relative humidity are negatively correlated with COVID-19 transmission throughout the world. Government intervention (e.g. lockdown policies) and lower population movement contributed to decrease the new daily case ratio. Weather conditions are not the decisive factor in COVID-19 transmission, in that government intervention as well as public awareness, could contribute to the mitigation of the spreading of the virus. So, it deserves a dynamic government policy to mitigate COVID-19 transmission in winter.

Analysis of Impact of Rain Conditions on ADAS.

Various technologies are being developed to support safe driving. Among them, ADAS, including LDWS, is becoming increasingly common. This driver assistance system aims to create a safe road environment while compensating for the driver's carelessness. The driver is affected by external environmental factors such as rainfall, snowfall, and bad weather conditions. ADAS is designed to recognize the surrounding situation and enable safe driving by using sensors, but it does not operate normally in bad weather conditions. In this study, we quantitatively measured the effect of bad weather conditions on the actual ADAS function. Additionally, we conducted a vehicle-based driving experiment to suggest an improvement plan for safer driving. In the driving experiment, when the vehicle driving speed was changed in four stages of rainfall, it was confirmed that it affected the View Range value, where the primary variable is the visibility of ADAS. As a result of the analysis, we demonstrated that when the rainfall exceeded a precipitation of 20 mm, the ADAS sensor did not operate, regardless of the vehicle speed. This means that a problem affecting safe driving may occur due to functionality in bad weather situations in which the driver requires ADAS assistance. Therefore, it is necessary to develop a technology that can maintain the minimum ADAS functionality under rainfall conditions and other bad weather conditions.

A Novel Target Detection Method of the Unmanned Surface Vehicle under All-Weather Conditions with an Improved YOLOV3.

The USV (unmanned surface vehicle) is playing an important role in many tasks such as marine environmental observation and maritime security, for the advantages of high autonomy and mobility. Detecting the targets on the surface of the water with high precision ensures the subsequent task implementation. However, the changes from the lights and the surface environment influence the performance of the target detecting method in a long-term task with USV. Therefore, this paper proposed a novel target detection method by fusing DenseNet in YOLOV3 to improve the stability of detection to decrease the feature loss, while the target feature is transmitted in the layers of a deep neural network. All the image data used to train and test the proposed method were obtained in the real ocean environment with a USV in the South China Sea during a one month sea trial in November 2019. The experiment results demonstrate the performance of the proposed method is more suitable for the changed weather conditions though comparing with the existing methods, and the real-time performance is available in practical ocean tasks for USV.

Short-term effects of multiple outdoor environmental factors on risk of asthma exacerbations: Age-stratified time-series analysis.

BACKGROUND: Although the different age groups had differences in sensitivity of asthma exacerbations (AEs) to environmental factors, no comprehensive study has examined the age-stratified effects of environmental factors on AEs. OBJECTIVE: We sought to examine the short-term effects in age-stratified groups (infants, preschool children, school-aged children, adults, and the elderly) of outdoor environmental factors (air pollutants, weather conditions, aeroallergens, and respiratory viral epidemics) on AEs. METHODS: We performed an age-stratified analysis of the short-term effects of 4 groups of outdoor environmental factors on AEs in Seoul Metropolitan City (Korea) from 2008 and 2012. The statistical analysis used a Poisson generalized linear regression model, with a distributed lag nonlinear model for identification of lagged and nonlinear effects and convergent cross-mapping for identification of causal associations. RESULTS: Analysis of the total population (n = 10,233,519) indicated there were 28,824 AE events requiring admission to an emergency department during the study period. Diurnal temperature range had significant effects in pediatric (infants, preschool children, and school-aged children) and elderly (relative risk [RR], 1.056-1.078 and 1.016, respectively) subjects. Tree and weed pollen, human rhinovirus, and influenza virus had significant effects in school-aged children (RR, 1.014, 1.040, 1.042, and 1.038, respectively). Tree pollen and influenza virus had significant effects in adults (RR, 1.026 and 1.044, respectively). Outdoor air pollutants (particulate matter of ≤10 µm in diameter, nitrogen dioxide, ozone, carbon monoxide, and sulfur dioxide) had significant short-term effects in all age groups (except for carbon monoxide and sulfur dioxide in infants). CONCLUSION: These findings provide a need for the development of tailored strategies to prevent AE events in different age groups.

Assessment of changes in the occurrence of Fusarium toxin and ochratoxin A in Poland related to extreme weather phenomena

Background: Mycotoxins ­ substances harmful to humans, are ubiquitous in the environment. Mycotoxins are generated primarily by Penicilium, Aspergillus and Fusarium genus fungi. Their presence is associated with the unavoidable presence of mold fungi in the environment. The presently observed adverse climatic changes could negatively affect agriculture, causing erosion and loss of organic matter from soil, promulgation of pests and plant diseases, including those originating from pathogenic molds, and also migration of certain mold species into new regions, ultimately creating more favorable conditions for generation of mycotoxins. Objective: The purpose of this work was to investigate contamination of cereals in Poland with Fusarium and ochratoxin A. Elucidating a correlation between precipitation levels in the individual Provinces and reported levels of the investigated mycotoxins, referring to the generally available meteorological databases, would result in more efficient planning of sampling processes and focusing further preventive actions associated with establishing sampling plans for the following years. Material and methods: Investigations were performed on cereal and cereal product samples taken by the official foodstuffs inspection staff. Some 100 samples were taken annually in the 2009-2012 period (357 samples in total). Tests were performed using high performance liquid chromatography coupled with mass spectrometry (HPLC-MS/MS). Precipitation data were obtained from the Central Office of Statistics, based on data received from the Institute of Meteorology and Water Management. Results: Analysis of the influence of precipitation levels during vegetation period on mycotoxin levels in the investigated foodstuffs was performed by associating each recorded content of deoxynivalenol (n=52, corresponding to 14.6% tested samples), zearalenone (n=30, 8.4%), total T-2 and HT-2 toxins (n=21, 5.9%) and ochratoxin A (n=88, 24.6%) above quantification limit with precipitation levels within the Province from which the sample originated. Deoxynivalenol and zearalenone levels show distinct variability corresponding with variability of precipitation levels, well reflecting the reported higher deoxynivalenol and zearalenone levels observed during the rainy years of 2011-2012. Variability in average ochratoxin A levels was not statistically significant. The relatively higher mycotoxin levels in 2009 may result from the heavy rainfall and flooding of 2007-2008. Dependence between the precipitation levels and number of samples showing levels above quantification limit has been also observed for deoxynivalenol. However, a similar analysis made for zearalenone and ochratoxin A does not point to any significant relationship. No data analysis was possible in reference to total T-2 and HT-2 toxins content due to the insufficient number of results available. However, it should be noted that 21% analyzed samples in 2009 contained T-2 and HT-2 levels above the quantification limit, with average of 8.9 µg/kg, whereas in 2010-2012 only one sample of the 263 tested contained contaminants in quantities above the quantification limit. Conclusions: The model used for forecasting presence of mycotoxins in cereals does not allow its practical application during routine generation of official control and monitoring plans on national scale. Notably, tests performed show that exceeding of maximum contamination levels occurred just incidentally, notwithstanding the adverse weather conditions. Further systematic collection of data on mycotoxin contamination of agricultural crops is required for effective continued investigations.

Quantifying the Relative Contributions of Environmental Sources to the Microbial Community in an Urban Stream under Dry and Wet Weather Conditions.

Investigating sources of microbial contamination in urban streams, especially when there are no contributions from combined sewer overflows or sewage effluent discharges, can be challenging. The objectives of this study were to identify the sources of microbes in an urban stream and quantify their relative contributions to the microbial community in the stream under dry and wet weather conditions. A microbial source tracking method relying on the 16S rRNA gene was used to investigate the microbial communities in water samples of an urban stream (i.e., from 11 dry and 6 wet weather events), as well as in streambed sediment, soils, street sweepings, sanitary sewage, an upstream lake, and feces of animals and birds collected between 2013 and 2015. The results showed that the Escherichia coli levels in the stream were significantly higher in wet weather flow than in dry weather flow. The upstream lake contributed approximately 93% of the microbes in dry weather flows. Water discharged from storm drain outfalls was the biggest source of microbes in wet weather flows, with a median contribution of approximately 90% in the rising limb and peak flow and about 75% in the declining limb of storms. Furthermore, about 70 to 75% of the microbes in the storm drain outfall water came from materials washed off from the street surfaces in the watershed. Fecal samples did not appear to contribute substantially to the microbes in environmental samples. The results highlight the significance of street surfaces in contributing microbial loads to urban streams under wet weather conditions.IMPORTANCE Identifying the sources of microbial contamination is important for developing best management practices to protect the water quality of urban streams for recreational uses. This study collected a large number of water samples from an urban stream under both dry and wet weather conditions and provided quantitative information on the relative contributions of various environmental compartments to the overall microbial contamination in the stream under the two weather conditions. The watershed in this study represents urban watersheds where no dominant fecal sources are consistently present. The findings highlight the importance of reducing the direct contribution of microbes from street surfaces in the watershed to urban streams under wet weather conditions. The methods and findings from this study are expected to be useful to stormwater managers and regulatory agencies.

Badger setts provide thermal refugia, buffering changeable surface weather conditions.

Den use can be crucial in buffering environmental conditions and especially to provide an insulated environment for raising altricial young. Through Sept-Dec 2016 we monitored temperature and humidity at 11 badger setts (burrow systems), using thermal probes inserted over 4-13 sett entrances to a depth of ca. 2 m, supplemented by continuous daily logging at one entrance per sett. Setts were cooler than exterior conditions Sept-Oct, and warmer than exterior conditions Nov-Dec. Setts cooled down when badgers left them to forage by night, and warmed up when badgers occupied them by day. Soil type and aspect also influenced sett temperature. Sett temperature did not affect the weight or body-condition of either adults or maturing cubs in autumn. However, cubs born into setts that were relatively warmer through the preceding autumn-winter were heavier in the following spring than contemporaries born in cooler setts (badgers exhibit delayed implantation), and so warmer setts might benefit early cub growth. We posit that sett quality may be important in providing badgers with a stable thermal refuge from variable weather conditions. More broadly, den use may buffer climate change effects for many fossorial carnivore species.

Reproductive and productive performances of Santa Inês ewes submitted to breeding in different periods of the Amazonian humid tropical climate.

The objective of this study was to evaluate the reproductive and productive performance of Santa Inês ewes bred at different times of the year in humid tropical climate. One hundred and forty-eight Santa Inês ewes were grouped according to the time of the year of their breeding season (i.e., mating period) (dry/wet, wet, wet/dry, and dry season). The service type was natural mating and the ewes and rams were kept together every night for 45 days. Reproductive efficiency was assessed by service, pregnancy, lambing, prolificacy, twinning, pregnancy loss, weaning, and lamb mortality rates. Ewes were weighed at the beginning and at the end of the breeding season and before and after parturition, and sequential weighing of the lambs was performed (at birth, 15, 30, 60, and 90 days). Reproductive efficiency index (number of lambs weaned/total of served ewes) and productive efficiency (kg of weaned lamb/kg of served or lambed ewes) were calculated. All ewes expressed estrus early in the breeding season; however, a higher percentage (53.5 and 7.1 % at 30 and 45 days, respectively) of ewes returned to estrus during the wet/dry period. The lower rates (13.9 %) of return to estrus at 30 days were during the wet season (P < 0.05). There were no (P > 0.05) effects of breeding seasons on the remaining reproductive rates. Ewes that lambed during the wet/dry transition period weighted less, before (40.5 ± 2.5 kg) and after (38.6 ± 1.6 kg) parturition, than those of other groups (P < 0.05). Lamb weight at birth did not vary between groups, however, weight at weaning was higher (15.6 ± 2.1 kg) in lambs born during the wet season (P < 0.05). The reproductive efficiency index was lower (0.66) when the breeding season took place during the dry/wet period (P < 0.05). Productive rates were significantly higher (0.29 and 0.33 for kg of weaned lamb/kg of served and lambed ewes, respectively; P < 0.05) in ewes served in the dry season. The reproductive performance of Santa Inês ewes was not significantly influenced by the period of the year in which the breeding seasons took place, allowing for four breeding seasons a year in the Amazon region. Variations between periods in return to estrus rates, weight of ewes close to parturition and lamb weight at weaning indicate that climate changes can also affect reproductive rates.

Predicting the type of road accidents based on air temperature in Iran: A case study of roads in Qazvin province.

This study investigates the relationship between ambient temperature, weather conditions, and types of road accidents in Qazvin province, Iran. The research addresses a significant societal challenge of road accidents, particularly in developing countries like Iran. The objectives are to analyze the correlation between temperature and accident types and to develop a predictive model using data mining techniques. The study employs a quantitative approach, analyzing over 15,000 accident records from 2010 to 2020. The findings reveal a connection between the temperature variable and the type of road accidents as well as weather conditions. Additionally, data mining analysis identifies a predictable pattern among temperature variables, types of road accidents, and weather conditions. Implications of the study underscore the importance of considering temperature and weather conditions as secondary factors influencing accidents. The predictive model can aid decision-makers in formulating effective strategies to reduce accidents. Understanding the relationship between temperature, weather, and accident types enables the design of targeted interventions to enhance road safety. This research contributes valuable insights to accident reduction efforts and emphasizes the significance of addressing environmental variables in road safety planning and policy-making. Moreover, the results of the data mining pattern analysis indicate that car overturning accidents in various weather conditions are the primary type of accidents, followed by chain accidents. However, the types of accidents vary based on different weather conditions and temperatures. The study highlights the intricate connection between weather conditions, temperature, and types of road accidents. By utilizing data mining techniques, the research provides a predictive model for accident patterns, offering valuable insights to enhance road safety strategies.

Examining the relationship between wind speed, climatic conditions, and road accidents in Iran.

This study investigates the relationship between wind speed, climatic conditions, and road accidents in Iran, focusing on the type of accidents and collisions. The research aims to identify the causes of accidents and provide insights for prediction and decision-making purposes. The study adopts a developmental research approach, analyzing road accident data and wind speed data. Logistic regression is employed to investigate the correlation between wind speed and the type of accidents and collisions. Data mining techniques, specifically the J48 decision tree algorithm, are used to examine the relationship patterns among wind speed, climatic conditions, collision types, and accident types. Additionally, texts and articles related to atmospheric hazards and road accidents are studied, and interviews are conducted with road accident experts and drivers to extract insights into the causes of road accidents in Iran. The findings indicate that wind speed does not have a direct and significant effect on the type of accidents (fatal or non-fatal), but it does influence the type of collisions in road accidents. The decision tree analysis reveals patterns in the relationships between weather conditions, wind speed, collision types, and accident types, enabling the prediction of collision probabilities in different scenarios. The causes of road accidents in Iran are categorized into human factors, secondary causes, and unique causes. Based on the findings, several recommendations are proposed to enhance road safety and reduce accidents in Iran.

Association between highly pathogenic avian influenza outbreaks and weather conditions in Japan.

Highly pathogenic avian influenza (HPAI) poses a significant threat to animal and public health, with outbreaks occurring globally. HPAI poses significant challenges due to its high mortality rate and public health concerns, with outbreaks spreading globally since the emergence of the H5N1 virus in 2003. In Japan, HPAI outbreaks have been particularly prevalent during autumn and winter seasons, with the 2022-2023 winter experiencing the most severe outbreak to date. However, limited research has directly examined the association between HPAI outbreaks and weather conditions in Japan. Here we show that specific weather conditions are associated with an increased risk of HPAI outbreaks on poultry farms in Japan. By analyzing databases of HPAI cases and meteorological data from 2020-2023, we found that higher average air temperatures two to three weeks prior, lower average wind speeds four weeks prior, and longer sunlight hours two and four weeks prior to outbreaks were significantly associated with increased risk of HPAI outbreaks in Japan. These results suggest that weather may influence environmental survival and transmission of the virus, as well as patterns of wild bird movement that could seed new outbreaks. These findings enhance our understanding of the factors influencing HPAI transmission dynamics and highlight the importance of integrating weather forecasts into disease surveillance and prevention strategies.

Controls for phytolith accumulation in Moso bamboo leaves across China.

Phytoliths are amorphous silica formed gradually in plant tissue, which have great potential to mitigate climate change due to their resistance to decomposition and their ability to occlude organic carbon. The accumulation of phytoliths is regulated by multiple factors. However, the factors controlling its accumulation remain unclear. Here, we investigated phytolith content in Moso bamboo leaves of different ages collected from 110 sampling sites of their main distribution regions across China. The controls for phytolith accumulation were studied by correlation and random forest analyses. Our results showed that phytolith content is leaf age-dependent (16-month-old leaf >4-month-old leaf >3-month-old leaf). Phytolith accumulation rate in Moso bamboo leaves is significantly correlated with mean monthly temperature (MMT) and mean monthly precipitation (MMP). About 67.1 % of the variance of the phytolith accumulation rate could be explained by multiple environmental factors, mainly MMT and MMP. Therefore, we conclude that the weather is the major driver that regulates the phytolith accumulation rate. Our study provides a unique dataset for estimating phytolith production rate and the potential carbon sequestration of phytolith through climatic factors.

Daily and Seasonal Activity Patterns of Plateau Pikas (Ochotona curzoniae) on the Qinghai-Tibet Plateau, China, and Their Relationship with Weather Condition.

Exploring the activity patterns of small mammals is important for understanding the survival strategies of these animals, such as foraging and mating. The purpose of the present study was to determine the activity of free-living plateau pikas (Ochotona curzoniae) in different months and seasons (cold and warm seasons), with a particular emphasis on the effects of weather condition. Based on a camera-trapping survey conducted from October 2017 to September 2018, we evaluated the activity patterns and activity levels of plateau pikas inhabiting the eastern Qinghai-Tibet Plateau in China. The effects of environmental factors on the activity of plateau pikas were examined using the generalized additive mixed model (GAMM). The results showed that: (1) The plateau pikas exhibited unimodal patterns of activity during the cold season (October-April). During the warm season (May-September), the activity patterns of the plateau pikas were bimodal. Their activity levels were highest in June. (2) During the cold season, their activity levels rose gradually over the course of the day to a peak near noon, and they were not significantly higher after sunrise than they were before sunset. During the warm season, their activity peaks were in the morning and afternoon, and their activity levels were substantially lower after sunrise than they were before sunset. (3) The plateau pikas were more active under conditions with lower ambient temperatures and precipitation during the cold and warm seasons. While relative air humidity was positively correlated with the activity of the plateau pikas during the warm season, wind speed was negatively correlated with the pikas' activity during the cold season. Overall, these results collectively indicate that plateau pikas occupy habitats with cool and less windy microclimates during the cold season, and with cool and moist microclimates during the warm season. Information on the time allocation of pikas' activity levels during different seasons should provide a baseline for understanding their potential for adaptation to climate change.

Exploring weather-related factors affecting the delay caused by traffic incidents: Mitigating the negative effect of traffic incidents.

BACKGROUND: Existing studies mainly focus on the relationship between real-time weather and traffic crash injury severity, while few scholars have investigated the operation risk levels caused by traffic incidents. Identifying weather-related factors that affect the incident-induced delay is helpful for estimating the delay levels when an incident occurs. Accordingly, the present study profoundly explores the relationship between weather conditions and traffic delays caused by traffic incidents. METHODS: The traffic incident and weather datasets from January 1 to December 31, 2020, in New York State are used. To that end, the hazard-based duration and multinomial logit modeling frameworks are employed to determine the effect of weather conditions on the duration of traffic delay and the delay severity, respectively. More importantly, to account for multiple layers of unobserved heterogeneity, a random parameter with heterogeneity in means approach is introduced into the above two models. RESULTS: (1) The strong breeze (wind speed over 8 m/s) and low visibility (visibility under 5 km) significantly affect the duration of delay. (2) Hot day (between 20 and 30 °C) has a 344.03 % greater probability of minor delay. A strong breeze has a higher probability of severe delay. The low visibility is found to increase the estimated odds of moderate delay and severe delay by 51.15 % and 13.39 %, respectively. In comparison, the normal visibility (between 10 and 20 km) significantly decreases the estimated odds of severe delay by 119.17 %. CONCLUSIONS: Compared with other weather factors, wind speed, temperature, and visibility have the greatest impact on the traffic delay levels after a traffic accident, and there are significant differences in the impact under different delay severity. Findings from this study will help policymakers to establish comprehensive differentiating security measures to resolve traffic delays.

The interplays among meteorology, source, and chemistry in high particulate matter pollution episodes in urban Shanghai, China.

High particulate matter (PM) pollution episodes still occur occasionally in urban China, despite of improvements in recent years. Investigating the influencing factors of high-PM episodes is beneficial in the formulation of effective control measures. We herein present the effects of weather condition, emission source, and chemical conversion on the occurrence of high-PM episodes in urban Shanghai using multiple online measurements. Three high-PM episodes, i.e., locally-accumulated, regionally-transported, and dust-affected ones, as well as a clean period were selected. Stagnant air with temperature inversion was found in both locally-accumulated and regionally-transported high-PM episodes, but differences in PM evolution were observed. In the more complicated dust-affected episode, the weather condition interacted with the emission/transport sources and chemical conversion, resulting in consecutive stages with different PM characteristics. Specifically, there were (1) stronger local accumulation in the pre-dust period, (2) dust-laden air with aged organic aerosol (OA) upon dust arrival, (3) pollutants being swept into the ocean, and (4) back to the city with aged OA. Our results suggest that (a) local emissions could be rapidly oxidized in some episodes but not all, (b) aged OA from long-range transport (aged in space) had a similar degree of oxygenation compared to the prolonged local oxidation (aged in time), and (c) OA aged over land and over the ocean were similar in chemical characteristics. The findings help better understand the causes and evolution of high-PM episodes, which are manifested by the interplays among meteorology, source, and chemistry, providing a scientific basis for control measures.

Association between hospitalizations for asthma exacerbation and weather conditions in Qingdao: an ecological study.

Background: The hospitalization for asthma exacerbation has varied with seasons, however, the underlying weather reasons have not been fully explored yet. This study is aimed to explore the effect of weather factors on increased number of hospitalization due to worsening of asthma symptoms. This will provide more information to the relevant authorities to allocate appropriate medical resources as per the weather conditions in Qingdao, China. Methods: All adult patients admitted for asthma exacerbation from 1 January, 2017 to 31 December, 2019 were enrolled from 13 main hospitals of Qingdao. The clinical data, including age, sex, smoking history, etc., were collected from the electronic medical record (EMR) systems. The hourly air quality of Qingdao from 2017-2019, including the air quality index (AQI), PM2.5 and PM10, was obtained from the China National Environmental Monitoring Centre. All these parameters during 2017-2019 were compared monthly. For meteorological data, the monthly horizontal wind at 850 hPa and vertical velocity at 500 hPa during 1960-2020 were obtained from National Center for Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR) global reanalysis dataset. The correlation analysis was applied to determine the association between asthma hospitalizations and the environmental factors, including atmospheric pressure, humidity, vertical visibility, and etc., monthly. Results: In all, 10,549 asthmatic inpatients (45.7% males, 54.3% females) were included in the study. The inpatients number for asthma exacerbation had a plateau lasting from March to June of 2019, accompanied with high PM2.5 and PM10, as well as bad air quality from January to March of 2019, potentially governed by the El Niño event in 2018. However, there was no significance correlation between the number of asthma hospitalizations and the average value of all environmental factors. Conclusions: The high rate of hospitalization for asthma exacerbation in Qingdao during the spring of 2019 was associated with the unfavorable weather conditions, which might be linked to the atmospheric circulation in East Asia.

Effects of black carbon aerosol on air quality and vertical meteorological factors in early summer in Beijing.

Black carbon (BC) aerosols have effects on the atmospheric thermal vertical structure due to its radiation absorption characteristics, hereby influencing the boundary layer characteristics and pollutant diffusion. This study focuses on the BC effects under different atmospheric conditions on air quality and vertical meteorological conditions. Four days flight observation combined with surface wind profiler radar data were used to investigate the vertical profiles of BC and wind speed over Beijing urban area in early summer. The vertical profiles of BC concentration and wind speed in the boundary layer had a negative correlation, both having abrupt changes near the boundary layer height under stagnant weather conditions. The chemical transport model showed the increase of BC under stagnant conditions could cause aggravation of the stability of the boundary layer, thereby increasing the accumulation of pollutants. In particular, BC leads to the changes in the temperature profile, which will modify relative humidity and indirectly lead to the changes in the vertical profile of aerosol optical properties. However, if the early accumulation of BC was absent under more turbulent conditions, the effects of BC on air quality and meteorological conditions were limited.