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In this paper, we evaluated the applicability of the minimum required compressive strength for cold weather concreting based on winter meteorological factors. In this study, a compressive strength test, dynamic elastic modulus test, hydration degree test, underwater weighing test, and freeze–thaw test were performed to investigate the effect of compressive strength development at early ages on frost resistance of concrete. In particular, the ASTM equivalent number of cycles (CyASTM−sp) of various locations was estimated based on winter meteorological factors. The results of experiments showed that the frost resistance of concrete at early ages increases with increased compressive strength. The relative dynamic modulus of elasticity of concrete of 5.0 MPa showed that it can be maintained above 90% within 18 freeze–thaw cycles. In addition, the CyASTM−sp results showed that a compressive strength of 5.0 MPa can protect concrete from early age frost damage in all investigated locations, indicating that a compressive strength of 5.0 MPa is the minimum required for safe and reliable cold weather concreting. However, for concrete structures subjected to repeated freeze–thaw cycles, it is necessary to select a higher compressive strength value according to the construction condition.

Co-occurring species may adopt different water-use strategies to adapt to limited soil water. In Jiuzhaigou Valley, a continuous decline in soil water after an initial recharge from the thawing of snow and frozen soil in early spring was observed, but its effects on the sap flow dynamics of co-occurring species are not well understood. To clarify the species-specific water-use strategy, variations in sap flow and environmental conditions were investigated for two co-occurring species (Betula albosinensis Burk. and Pinus tabuliaeformis Carr.) in a mixed forest during a transition from the wet to dry period in 2014. Sap flow was measured using Granier-type thermal dissipation probes, and the soil-water content was measured using time-domain reflectometry probes for a successive period. Our study showed that B. albosinensis maintained relatively high transpiration until late into the season regardless of soil moisture, while the transpiration of P. tabuliformis showed a continuous decrease in response to seasonal soil drying. Sap flow for both species exhibited a marked hysteresis in response to meteorological factors and it was conditioned by the soil-water status, especially in the afternoon. We found that P. tabuliformis was sensitive to soil-water conditions, while for B. albosinensis, the sap flow was not very sensitive to changes in soil-water conditions. These results indicate that B. albosinensis could manage the water consumption conservatively under both dry and wet conditions. These results may have implications for evaluating the species-specific water-use strategy and carrying out proper reforestation practices.

The interactions between PM2.5 and meteorological factors play a crucial role in air pollution analysis. However, previous studies that have researched the relationships between PM2.5 concentration and meteorological conditions have been mainly confined to a certain city or district, and the correlation over the whole of China remains unclear. Whether spatial and seasonal variations exist deserves further research. In this study, the relationships between PM2.5 concentration and meteorological factors were investigated in 68 major cities in China for a continuous period of 22 months from February 2013 to November 2014, at season, year, city, and regional scales, and the spatial and seasonal variations were analyzed. The meteorological factors were relative humidity (RH), temperature (TEM), wind speed (WS), and surface pressure (PS). We found that spatial and seasonal variations of their relationships with PM2.5 exist. Spatially, RH is positively correlated with PM2.5 concentration in north China and Urumqi, but the relationship turns to negative in other areas of China. WS is negatively correlated with PM2.5 everywhere except for Hainan Island. PS has a strong positive relationship with PM2.5 concentration in northeast China and mid-south China, and in other areas the correlation is weak. Seasonally, the positive correlation between PM2.5 concentration and RH is stronger in winter and spring. TEM has a negative relationship with PM2.5 in autumn and the opposite in winter. PS is more positively correlated with PM2.5 in autumn than in other seasons. Our study investigated the relationships between PM2.5 and meteorological factors in terms of spatial and seasonal variations, and the conclusions about the relationships between PM2.5 and meteorological factors are more comprehensive and precise than before. We suggest that the variations could be considered in PM2.5 concentration prediction and haze control to improve the prediction accuracy and policy efficiency.

By collecting daily data on measles cases, air pollutants, and meteorological data from 2005 to 2009 in Chengguan District of Lanzhou City, semi-parametric generalized additive model (GAM) was used to quantitatively study the impact of air pollutants and meteorological factors on daily measles cases. The results showed that air pollutants and meteorological factors had effect on the number of daily measles cases, and there was a certain lag effect. Except for SO 2 and relative humidity, other factors showed statistically significant associations with daily measles cases: NO 2 lag 6 days, PM 10 and maximum temperature lag 5 days, minimum temperature and average temperature and average air pressure lag 4 days, visibility, and wind speed lag 3 days had the greatest impact on the number of daily measles cases. Under the optimum lag conditions, the number of daily measles cases increased by 15.1%, 17.6%, 7.0%, 116.6%, 98.6%, 85.7%, and 14.4% with the increase of 1 IQR in SO 2 , NO 2 , PM 10 , maximum temperature, minimum temperature, average temperature, and wind speed; with the increase of 1 IQR in average pressure, relative humidity, visibility, and daily measles cases decreased by 12.8%, 9.7%, and 13.1%, respectively. And different factors showed different seasonal effects. The effects of SO 2 and temperature factors on daily measles cases were greater in spring and winter, but PM 10 in summer.

The outbreak of coronavirus disease 2019 (COVID-19), caused by the virus SARS-CoV-2, has been rapidly increasing in the United States. Boroughs of New York City, including Queens county, turn out to be the epicenters of this infection. According to the data provided by the New York State Department of Health, most of the cases of new COVID-19 infections in New York City have been found in the Queens county where 42,023 people have tested positive, and 3221 people have died as of 20 April 2020. Person-to-person transmission and travels were implicated in the initial spread of the outbreaks, but factors related to the late phase of rapidly spreading outbreaks in March and April are still uncertain. A few previous studies have explored the links between air pollution and COVID-19 infections, but more data is needed to understand the effects of short-term exposures of air pollutants and meteorological factors on the spread of COVID-19 infections, particularly in the U.S. disease epicenters. In this study, we have focused on ozone and PM2.5, two major air pollutants in New York City, which were previously found to be associated with respiratory viral infections. The aim of our regression modeling was to explore the associations among ozone, PM2.5, daily meteorological variables (wind speed, temperature, relative humidity, absolute humidity, cloud percentages, and precipitation levels), and COVID-19 confirmed new cases and new deaths in Queens county, New York during March and April 2020. The results from these analyses showed that daily average temperature, daily maximum eight-hour ozone concentration, average relative humidity, and cloud percentages were significantly and positively associated with new confirmed cases related to COVID-19; none of these variables showed significant associations with new deaths related to COVID-19. The findings indicate that short-term exposures to ozone and other meteorological factors can influence COVID-19 transmission and initiation of the disease, but disease aggravation and mortality depend on other factors.

Air quality forecasting is a significant method of protecting public health because it provides early warning of harmful air pollutants. In this study, we used correlation analysis and artificial neural networks (ANNs; including wavelet ANNs [WANNs]) to identify the linear and nonlinear associations, respectively, between the air pollution index (API) and meteorological variables in Xi’an and Lanzhou. Evaluating twelve algorithms and nineteen network topologies for the ANN and WANN models, we discovered that the optimal input variables for an API forecasting model were the APIs from the 3 preceding days and sixteen selected meteorological factors. Additionally, the API could be accurately predicted based solely on the value recorded 3 days earlier. Based on the correlation coefficients between the air pollution index of the targeted day and the tested variables, the API displayed the closest relationship with the API 1 day earlier as well as stronger correlations with the average temperature, average water vapor pressure, minimum temperature, maximum temperature, API 2 days earlier, and API 3 days earlier. When Bayesian regularization was applied as a training algorithm, the WANN and ANN models accurately reproduced the APIs in both Xi’an and Lanzhou, although the WANN model (R = 0.8846 for Xi’an and R = 0.8906 for Lanzhou) performed better than the ANN (R = 0.8037 for Xi’an and R = 0.7742 for Lanzhou) during the forecasting stage. These results demonstrate that WANNs are effective in short-term API forecasting because they can recognize historic patterns and thereby identify nonlinear relationships between the input and output variables. Thus, our study may provide a theoretical basis for environmental management policies.

The context of rapid global environmental change underscores the pressing necessity to investigate the environmental factors and high-risk areas that contribute to the occurrence of brucellosis. In this study, a maximum entropy (MaxEnt) model was employed to analyze the factors influencing brucellosis in the Aksu Prefecture from 2014 to 2023. A distributed lag nonlinear model (DLNM) was employed to investigate the lagged effect of meteorological factors on the occurrence of brucellosis. A total of 17 environmental factors were identified as affecting the distribution of brucellosis to varying degrees. The largest contributing was the normalized difference vegetation index (NDVI), followed by gross domestic product (GDP), and then meteorological factors such as average temperature, average relative humidity, and average wind speed. The receiver operating characteristic (ROC) curve demonstrated that the MaxEnt model exhibited a high degree of predictive efficacy, with an area under the curve (AUC) value of 0.921. The impact of high temperature (25℃ with a 2-month lag, RR = 3.130, 95% CI 1.642 ~ 5.965), low relative humidity (28% with a 2.5-month lag, RR = 1.795, 95% CI 1.298 ~ 2.483), and low wind speed (1.9 m/s with a 0-month lag, RR = 2.408, 95% CI 1.360 ~ 4.264) are the most significant meteorological factors associated with the incidence of brucellosis. The trends in the impact of extreme meteorological conditions on the spread of brucellosis were found to be generally consistent. Stratified analyses indicated that males were more affected by meteorological factors than females. The prevalence of brucellosis is influenced by a range of socio-economic and meteorological factors, and a multifaceted approach is necessary to prevent and control brucellosis.

Gannan is the largest navel orange production area in China. Most studies have primarily focused on the effects of either soil or topographic factors on the quality of navel oranges. However, there has been a lack of research exploring the relationship between navel orange quality and multiple environmental factors (meteorological, topographic, and soil). This study focused on Gannan navel oranges, selecting standard orchards in the core navel orange-producing area as the research region. It employed the Partial Least Squares Regression (PLSR) method to investigate the extent of the impact of environmental factors on fruit quality. The results indicated that the effect of soil factors on fruit shape and fruit flavor was greater than that of meteorological and topographic factors in the Gannan area. And the fruit peel is more uniformly influenced by environmental factors. Based on the degree of impact of various environmental factors, multiple regression equations for fruit quality have been established to identify the optimal conditions conducive to the comprehensive development of Gannan navel oranges. These findings help determine the optimal planting areas for Gannan navel oranges, providing practical evidence for the future development of navel oranges.