Influence of meteorological conditions on PM2.5 concentrations across China: A review of methodology and mechanism

Effects of meteorological conditions and anthropogenic precursors on ground-level ozone concentrations in Chinese cities

In observational studies, a possible correlation between atmospheric environmental factors and the number of daily outpatient visits by Otitis media with effusion(OME) patients has been observed. However, the causal relationship is not clear.To study the relationship between the incidence of OME and meteorological factors and air pollutants in the main urban areas of Lanzhou, it is helpful to further understand the health effects of meteorological and environmental factors on OME and to prevent and treat the disease, it is of great academic and practical significance to the prevention, treatment and prognosis of diseases. The levels of AQI、PM2.5、PM10、NO2、O3、SO2、CO、AP、RH、W and T were obtained from local monitor stations. Data of patients with OME were collected from two Grade A Level hospitals in Lanzhou from January 1, 2014 to December 31, 2016. Descriptive analysis of data was carried out for the study subjects. Spearman correlation coefficients between atmospheric environmental factors and daily visits of patients with OME were calculated by SPSS statistical software. Lag effects, relative risks(RR) and exposure-response curves were calculated by generalized additive model (GAM) with R software. (1) The incidence of OME in winter and spring was more than that in summer and autumn, which was consistent with the seasonal variation of meteorological environmental factors of Lanzhou. That was, the meteorological conditions and air quality in winter and spring were poor, while in summer and autumn they were relatively good. (2) The number of male outpatients were 1.05, 1.08 and 1.09 times of female outpatients during the period 2014-2016, respectively. And aged 0-10 years old outpatients accounted for 31% of the total OME outpatients. (3) Exposure-response curve showed that PM2.5, PM10, NO2 and SO2 were positively correlated with OME, T was negatively correlated with OME. When concentration < 1mg/m3, CO was positively correlated with OME. When concentration>1mg/m3, CO was negatively correlated with OME. When concentration<30 ug/m3, O3 was positively correlated with OME. When concentration>30ug/m3, O3 was negatively correlated with OME. (4) The factors we studied could significantly affect the number of OME outpatients within 2-3 days of single lag effects, and 3-4 days of cumulative lag effects.(5) The influential factors on OME were as follows: PM2.5、NO2、SO2、O3、 CO and T. The daily average number of OME patients in different seasons was different in major region of Lanzhou city, with more in winter、spring and fewer in summer、autumn. Age and sex were the important factors affecting the daily average number of OME patients. Males were more susceptible to OME than females and children awere moresusceptible to OME than adults. The change of OME patients was related to air quality, air pressure and temperature. The worse the air quality, the higher the air pressure, the lower the temperature, the more the average daily number of OME patients. Meteorological environmental factors affected the visits of OME, and the lagging effect time of different factors were different. Most of the research factors within 3-4 days had a significant impact on the number of patients of OME. 1.The number of OME visits in the Lanzhou was more seasonal in winter and spring than in summer and fall. 2.Age and sex were the most important factors affecting the number of patients with OME. According to the prevalence of OME in Lanzhou, children were more likely to have OME than adults and men were more likely to have OME than women. 3.The number of OME patients was related to air quality, air pressure and temperature. 4.The meteorological factors have a delayed effect on the onset of OME, and the time of delayed effect is different for different factors. The single delayed effect of 2-3 days and the cumulative delayed effect of 3-4 days have a significant effect on the change of the number of patients with OME. To study the relationship between the incidence of OME and meteorological factors and air pollutants in the main urban areas of Lanzhou, it is helpful to further understand the health effects of meteorological and environmental factors on OME and to prevent and treat the disease, it is of great academic and practical significance to the prevention, treatment and prognosis of diseases.

Understanding long-term variations of meteorological influences on ground ozone concentrations in Beijing During 2006–2016

Influence of meteorological conditions and particulate matter on visual range impairment in Jinan, China

Meteorological factors' effects on COVID-19 show seasonality and spatiality in Brazil

Abstract. China has long-term high PM2.5 levels, and its oxidative potential (OP) is worth studying as it may unravel the impacts of aerosol pollution on public health better than PM2.5 alone. OP refers to the ability of PM2.5 to induce oxidative stress (OS). OP and PM2.5 are influenced by meteorological factors, anthropogenic emission sources, and atmospheric aging. Although their impact on PM2.5 has been studied, OP measurements only recently became available and on a limited scale, as they require considerable technical expertise and resources. For this, the joint relationship between PM2.5 and OP for a wide range of meteorological conditions and emission profiles remain elusive. Towards this, we estimated PM2.5 and OP over China using the Danish Eulerian Hemispheric Model (DEHM) system with meteorological input from the Weather Research and Forecasting (WRF) model. It was found that higher values of PM2.5 and OP were primarily concentrated in urban agglomerations in the central and eastern regions of China, while lower values were found in the western and northeastern regions. Furthermore, the probability density function revealed that about 40 % of areas in China had annual average PM2.5 concentrations exceeding the Chinese concentration limit. For OP, 36 % of the regions have OP below 1 nmolmin-1m-3, 41 % have OP between 1 and 2 nmolmin-1m-3, and 23 % have OP above 2 nmolmin-1m-3, which are in line with previous measurement studies. Analysis of the simulations indicates that meteorological conditions contributed 46 % and 65 % to PM2.5 concentrations and OP variability, respectively, while anthropogenic emissions contributed 54 % and 35 % to PM2.5 concentrations and OP variability, respectively. The emission sensitivity analysis also highlighted the fact that PM2.5 and OP levels are mostly determined by secondary aerosol formation and biomass burning.

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.

Air pollution gained special attention with the rapid development in Beijing. In January 2013, Beijing experienced extreme air pollution, which was not well examined. We thus examine the magnitude of air quality in the particular month by applying the air quality index (AQI), which is based on the newly upgraded Chinese environmental standard. Our finding revealed that (1) air quality has distinct spatial heterogeneity and relatively better air quality was observed in the northwest while worse quality happened in the southeast part of the city; (2) the wind speed is the main determinant of air quality in the city—when wind speed is greater than 4 m/sec, air quality can be significantly improved; and (3) urban impervious surface makes a contribution to the severity of air pollution—that is, with an increase in the fraction of impervious surface in a given area, air pollution is more severe. The results from our study demonstrated the severe pollution in Beijing and its meteorological and landscape factors. Also, the results of this work suggest that very strict air quality management should be conducted when wind speed less than 4 m/sec, especially at places with a large fraction of urban impervious surface.Implications: Prevention of air pollution is rare among methods with controls on meteorological and urban landscape conditions. We present research that utilizes the latest air quality index (AQI) to compare air pollution with meteorological and landscape conditions. We found that wind is the major meteorological factor that determines the air quality. For a given wind speed greater than 4 m/sec, the air quality improved significantly. Urban impervious surface also contributes to the severe air pollution: that is, when the fraction of impervious surface increases, there is more severe air pollution. These results suggest that air quality management should be conducted when wind speed is less than 4 m/sec, especially at places with a larger fraction of urban impervious surface.

To analyze the relationship between levels of air pollution and number of children hospitalizations because of respiratory tract infection in Shenmu County, the data regarding meteorological factors, environmental pollutants, that is SO2 and NO2, Particulate Matter 10 (PM10), and hospitalizations of children less than 16 years of age was collected during the time duration of November 2009 to October 2012. Using SAS 9.3, descriptive data analysis for meteorological and environmental factors and hospital admissions were performed along with main air pollutants determination. Using the statistical software R 3.0.1, a generalized additive Poisson regression model was established, the linear fitting models of the air pollutant concentrations and meteorological factors were introduced considering the lag effect, and the relative risk of the main atmospheric pollutants on children hospitalization was evaluated. The results showed that the primary air pollutant in Shenmu County is PM10 and its Pearson correlation coefficient with Air Pollution Index (API) is 0.917. After control of long term climate trend, “week day effect,” meteorological factors, and impact of other contaminants, it was found that, on the same day and during the lag of 1 to 10 days, PM10 concentrations had no significant effect on children hospitalization rate.

The relationship between meteorological conditions and crop yields is mainly aimed at explaining the impact of climate change on the environment. Fluctuations in air temperature and humidity affect the water balance and are the main factors for increasing or decreasing crop yields. Such hydrothermal variations can affect any region. Despite this, the purpose of the study was to establish the influence of meteorological conditions such as temperature, precipitation, the sum of active temperatures, and the Selyaninov hydrothermal coefficient (HTC) on the yield and quality of seeds of different varieties of differently ripened soybeans Glycine max (L.) Merril of foreign selection in the conditions of the central Forest-Steppe. The study used general scientific and special research methods, in particular, field, laboratory, and statistical. The highest yield on average over the years of research was observed in the standard Mentor variety (2.45 t/ha), which was slightly higher than the Cordoba variety (&lt;1%). The deviation of the target for the Moravia variety from the standard variety was 7.4%. According to the results, the lowest yield variability was found in the standard Mentor variety (2.57%), the Cordoba variety was also characterised by low variability (5.99%), while in the Moravia variety, this indicator was with an average variability of 17.62%. During the three-year research period, the protein content in soybean seeds ranged from 38.75% to 40.31%. All soybean varieties had low trait variability (&lt;3%). The analysis of the relationship between the yield and quality of seeds of soybean varieties of foreign selection and the main factors of climatic conditions formed in the central Forest-Steppe region was carried out. Correlation analysis showed the degree of influence of various meteorological conditions on the productivity of the soybean varieties under study, which facilitates a more purposeful and balanced approach to the choice of varieties in specific climatic conditions. The results of the conducted studies can be applied in the implementation of measures for a reasonable choice of soybean varieties to ensure consistently high yields during periods of possible adverse meteorological conditions

Impact of diurnal variability and meteorological factors on the PM2.5 - AOD relationship: Implications for PM2.5 remote sensing

This study aimed to understand the effects of meteorological factors on the ‘Fuji’ apple quality in the Circum-Bohai and Loess Plateau apple production regions of China and to guide apple production based on local climate. Fruit samples of the ‘Fuji’ apple and meteorological data were investigated from 132 commercial ‘Fuji’ apple orchards covering 44 counties in the two aforementioned production regions (22 counties per region). The partial least-squares regression (PLSR) method was first used to screen major meteorological factors that greatly affected fruit quality; these were subsequently used to establish the regression equation of fruit quality attributes and major meteorological factors. Linear programming was used to estimate optimum meteorological factors for good apple quality. The results showed that in the Circum-Bohai production region, many meteorological factors (total annual precipitation, total precipitation from April to October, lowest temperature from April to October, sunshine percentage from April to October) were significantly higher than those in the Loess Plateau production region; however, the temperature difference between day and night from April to October was significantly smaller than that in the Loess Plateau production region. The soluble solids content and skin color area of apples from the Loess Plateau production region were significantly greater than those from the Circum-Bohai production region. The same fruit quality factor of ‘Fuji’ apple was affected by different meteorological factors in the two production regions. The monthly mean temperature and monthly highest temperature from April to October of the Circum-Bohai production region had relatively larger positive effect weights on fruit quality, whereas the total annual precipitation, monthly mean relative humidity from April to October, and total precipitation from April to October of the Loess Plateau production region had relatively larger positive effect weights on fruit quality. The major influencing meteorological factors of the fruit soluble solids content were total precipitation from April to October ( X 7 ), mean annual temperature ( X 1 ), and the monthly highest temperature from April to October ( X 5 ) in the Circum-Bohai production region; however, it included the monthly mean temperature difference between day and night from April to October ( X 6 ), total annual precipitation ( X 2 ), and total precipitation from April to October ( X 7 ) in the Loess Plateau production region. In the Circum-Bohai production region, the optimum meteorological factors for ‘Fuji’ fruit quality of vigorous apple orchards were the mean annual temperature (13.4 °C), total annual precipitation (981 mm), monthly mean temperature (16.8 to 22.4 °C), lowest temperature (11.9 °C), highest temperature (19.5 to 26.8 °C), temperature difference between day and night (12.3 °C), total precipitation (336–793 mm), relative humidity (55.7% to 70.7%), and sunshine percentage (42.3% to 46.1%) during the growing period (April–October). In the Loess Plateau production region, the optimum meteorological factors for ‘Fuji’ fruit quality of vigorous apple orchards were the mean annual temperature (5.5 to 11.6 °C), total annual precipitation (714 mm), monthly mean temperature (13.3 to 19.9 °C), lowest temperature (7.9 to 9.3 °C), highest temperature (19.6 to 27.3 °C), temperature difference between day and night (7.1 to 12.4 °C), total precipitation (338–511 mm), relative humidity (56.1% to 82.4%), and sunshine percentage (37.3% to 55.9%) during the growing period (April–October). The restrictive factors for high-quality ‘Fuji’ apples of the Circum-Bohai production region were the smaller monthly mean temperature difference between day and night, higher monthly mean lowest temperature, and larger monthly mean relative humidity during the growing period; however, those of the Loess Plateau production region were drought or less precipitation from November to March, lower monthly mean temperature, and higher monthly mean highest temperature during the growing period.

Global air quality change during the COVID-19 pandemic: Regionally different ozone pollution responses COVID-19: 疫情期间全球空气质量变化:臭氧响应的区域间差异

The goal of this study was to improve the accuracy of model forecasting, such that forecasters could use model products to make more efficient daily weather predictions. Historical data of the 12 hr following a given time for various meteorological factors from the control forecasts of the European Centre for Medium‐Range Weather Forecasting (ECMWF) between 20 ° and 40 ° N latitude and 110 °–130 ° E longitude were used to verify the performance of the proposed method. Eight major meteorological factors were selected via correlation analysis between control forecast meteorological factors and real‐time rainfall. The samples were divided into four types using the K‐means clustered method. Each type was respectively modelled by long short‐term memory (LSTM) in order to correct rainfall forecasts for eastern China. The eight major meteorological factors were used as the model input, and the differences between real‐time rainfall data and model‐forecast rainfall were used as the model output. The corrected results revealed that the root mean square error decreased by 0.65, and the threat scores of light rainfall and rainstorms were improved.

Pneumothorax and the environment: A systematic review of the impact of air pollution and meteorology, and a meta-analysis on meteorology factors