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Rice is a major staple food grain for more than half of the world's population, and China is the largest rice producer and consumer in the world. In a climate-warming context, the frequency, duration and intensity of heat waves tend to increase, and rice production will be exposed to higher heat damage risks. Understanding the negative impacts of climate change on the rice supply is a critical issue. In this study, a new perspective on agricultural weather index insurance is proposed to investigate the impact of extreme high-temperature events on rice production in South China in the context of climate change. Based on data from meteorological stations in Anhui Province in China from 1961 to 2018 and the projected data from five Global Climate Models under three representative concentration pathway (RCP) scenarios from 2021 to 2099, the spatial–temporal characteristics of heat stress and its influence on rice production were analyzed by employing a weather index insurance model. The interdecadal breakpoints in the trends of the heat stress weather insurance index (HSWI) and the payout from 1961 to 2018 in 1987 were both determined, which are consistent with the more significant global warming since the 1980s. The largest increase after 1987 was found in the southeastern part of the study area. The projected HSWI and the payout increased significantly from 2021 to 2099, and their growth was faster with higher radiative forcing levels. The HSWI values were on average 1.4 times, 3.3 times and 6.1 times higher and the payouts were on average 3.9 times, 9.8 times and 15.0 times higher than the reference values for the near future, mid-future and far future, respectively. The results suggest that a more severe influence of heat damage on rice production will probably happen in the future, and it is vital to develop relevant adaptation strategies for the effects of a warmer climate and heat stress on rice production. This paper provides an alternative way to transform the evaluation of the extreme climate event index into the quantitative estimation of disaster impacts on crop production. [ABSTRACT FROM AUTHOR]

Nitrogen dioxide (NO2) is one of the major air pollutants in cities across mid-eastern China. Comprehending the spatial and temporal dynamics of NO2 drivers in various urban areas is imperative for tailoring effective air control strategies. Using data from ground-based monitoring stations, we investigated the impact of socioeconomic and meteorological factors on NO2 concentrations in cities in mid-eastern China from 2015 to 2021 using the Geographically and Temporally Weighted Regression (GTWR) model. The findings reveal a notable reduction of over 10% in NO2 concentrations since 2015 in most cities, notably a 50.5% decrease in Bozhou. However, certain areas within Anhui and Jiangsu have experienced an increase in NO2 concentrations. Significant spatial heterogeneity is observed in the relationship between NO2 concentrations and influencing factors. The permanent population density (POP) and the electricity consumption (EC) of the entire society exhibited the strongest correlations with NO2 concentrations, with average coefficients of 0.431 and 0.520, respectively. Furthermore, other economic factors such as urbanization rate (UR), the share of secondary sector output in total GDP (IS), and the coverage rate of urban green areas (CG) were predominantly positively correlated, while GDP per capita (PGDP) and civil car vehicles (CV) demonstrated primarily negative correlations. Furthermore, we examined the correlations between four meteorological factors (temperature, relative humidity, wind speed, and precipitation) and NO2 concentrations. All these factors exhibited negative correlations with NO2 concentrations. Among them, temperature exhibited the strongest negative correlation, with a coefficient of −0.411. This research may contribute valuable insights and guidance for developing air emission reduction policies in various cities in mid-eastern China. [ABSTRACT FROM AUTHOR]

By using the daily average wind speed and precipitation data of 125 stations in Jiangsu and Anhui Provinces of China from 1961 to 2020 and the monthly NCEP/NCAR reanalysis data, the interannual variation characteristics and its possible reasons of spring compound extreme wind and precipitation events in the Jiangsu–Anhui region were discussed. Results show that the spring compound extreme wind and precipitation events generally present a lesser distribution in the south and more in the north. The events occurring in south (north) of 32° N are basically less than (above) three days, and in some areas of northern Jiangsu, it can reach more than four days. On a regional average, the spring compound extreme wind and precipitation events have presented a significant downward trend in the past 60 years. In addition, there was an interdecadal mutation from more to less in the early 1990s, with the most significant decline in the coastal areas of northern Jiangsu. Further analysis reveals that the synthetic height anomaly field at 500 hPa corresponding to the frequent occurrence of the spring compound extreme wind and precipitation events is positive in the northern region of 45° N, while it is negative in the southern region of 45° N, which enhances the high pressure in high latitudes, increases the meridional gradient of circulation, and promotes the activity of high-latitude short-wave trough ridges and cold air. Meanwhile, a strong southwest airflow exists in the corresponding middle and low latitudes at 850 hPa, so the water vapor from the Bay of Bengal can be continuously transported to the Jiangsu–Anhui region. Overall, the abundant water vapor transportation and the convergence of southward cold air in high latitudes are conducive to the occurrence of extreme wind and precipitation events. [ABSTRACT FROM AUTHOR]

Based on meteorological observations, traffic flow data and information of traffic accidents caused by fog or agglomerate fog along the expressways in Jiangsu Province and Anhui Province in China from 2012 to 2021, key impact factors including meteorological conditions, road hidden dangers and traffic flow conditions are integrated to establish the prediction model for risk levels of expressway agglomerate fog-related accidents. This model takes the discrimination of the occurrence conditions of agglomerate fog as the starting term, and determines the hazard levels of agglomerate fog-related accidents by introducing the probability prediction value of meteorological conditions for fog-related accident as the disaster-causing factor. On this basis, the hourly road traffic flow and the location of road sections with a hidden danger of agglomerate fog are taken as traffic and road factors to construct the correction scheme for the hazard levels, and the final predicted risk level of agglomerate fog-related accident is obtained. The results show that for the criteria of disaster-causing factor classification threshold, 72.3% of fog-related accidents correspond to a hazard of a medium level or above, and 86.2% of the road traffic flow conditions are consistent with the levels of the traffic factor defined based on parametric indexes. For risk level prediction, six out of the seven agglomerate fog-related accidents correspond to the level of higher risk or above, which can help provide meteorological support for traffic safety under severe weather conditions. Moreover, the model takes into account the impacts of traffic flow and the road environment, which is conducive to further improving the reliability of the risk assessment results. [ABSTRACT FROM AUTHOR]

Handan is a typical city affected by regional particulate pollution. In order to investigate particulate matter (PM) characterization, source contributions and health risks for the general populations, we collected PM samples at two sites affected by a pollution event (12–18 May 2020) during the COVID-19 pandemic and analyzed the major components (SNA, OCEC, WSIIs, and metal elements). A PCA-MLR model was used for source apportionment. The carcinogenic and non-carcinogenic risks caused by metal elements in the PM were assessed. The results show that the renewal of old neighborhoods significantly influences local PM, and primarily the PM10; the average contribution to PM10 was 27 μg/m3. The source apportionment has indicated that all other elements came from dust, except Cd, Pb and Zn, and the contribution of the dust source to PM was 60.4%. As PM2.5 grew to PM10, the PM changed from basic to acidic, resulting in a lower NH4+ concentration in PM10 than PM2.5. The carcinogenic risk of PM10 was more than 1 × 10−6 for both children and adults, and the excess mortality caused by the renewal of the community increased by 23%. Authorities should pay more attention to the impact of renewal on air quality. The backward trajectory and PSCF calculations show that both local sources and short-distance transport contribute to PM—local sources for PM10, and short-distance transport in southern Hebei, northern Henan and northern Anhui for PM2.5, SO2 and NO2. [ABSTRACT FROM AUTHOR]

In this study, the water-soluble inorganic ions (WSIIs) composition of fine particulate matter (PM2.5) was measured in the northern Nanjing city from 2015 to 2021. NH4+, NO3− and SO42− concentrations dominated in total WSIIs (Na+, NH4+, K+, Mg2+, Ca2+, Cl−, NO3− and SO42−), accounting for 87.8%. The nitrate with highest average concentration among all ions was 11.0 μg·m−3. Total WSIIs concentrations were higher in winter and lower in summer, with the highest levels in December (45.6 μg·m−3) and the lowest levels in August (15.1 μg·m−3). NO3−/SO42− was higher than 1, indicating the important contribution of mobile sources. The aerosols exhibited a weak acidic by the molar ratio of water-soluble anions and cations. Positive matrix factorization (PMF) analysis results showed that secondary nitrate and sulfate were the major pollution sources in December 2016 and 2020. The contribution of secondary nitrate in 2020 increased by 47.6% compared to 2016, while that of secondary sulfate decreased by 42.4%. The potential source contribution results demonstrated that for secondary aerosol concentrations, the contribution of regional transport from north of Anhui increased, while the contribution of local emissions decreased. The results from this study could contribute to the better prevention and control of regional air pollution in the future. [ABSTRACT FROM AUTHOR]

Abstract: Lightning forecasting is a vital item in server convective system short-time forecasting. However, lightning parameterization in mesoscale numerical prediction models is still in its early stages of development. Several lightning parameterization schemes are implemented in the Weather Research and Forecasting (WRF) model. Data assimilation can provide a more accurate initial field, which could be useful for subsequent lightning forecasting. To evaluate its effect on lightning forecasting, a severe convective case that influenced Jiangsu and Anhui Province on 5 June 2009 is utilized and a series of experiments are conducted including assimilating radar reflectivity and lightning location network data via the three-dimensional variational (3DVar) method. Results show that data assimilation can effectively improve reflectivity forecasting and subsequent lightning forecasting. Lightning forecasting based on the PR92 lightning parameterization scheme, which is based on the convective cloud top height, offers a weaker magnitude forecast. The diagnostic method based on reflectivity and temperature has some spatial displacement. The potential forecast provided by lightning threat indexes produced an improvement in Anhui Province, while in other regions, it is located further east than the observation. [ABSTRACT FROM AUTHOR]

Chlorine (Cl) released from coal-fired power plants can be harmful to power equipment, the ecological environment and human health. Here, we investigated the Cl releasing characteristics from four coal-fired power plants (CFPPs) in China's Anhui province based on an ion chromatography analysis of the combustion by-production samples collected from different locations of the power plants. The results showed that Cl content in coals was low (198–367 μg·g−1), which positively correlated with the contents of lead, mercury and total sulfur, but was weakly correlated with the moisture and ash yield in coal. The releasing rate of Cl during coal combustion was highly correlated with temperature and volatile matter, and most Cl is transferred into the flue gas. Dust collector and wet flue gas devices equipped in the CFPPs were robust for removing Cl in the particulate phase, and the fabric filter showed a higher removal efficiency than the electrostatic precipitator. This study can provide theoretical support for Cl pollution control in coal-fired power plants. [ABSTRACT FROM AUTHOR]

Diesel-powered agricultural machinery (AM) is a significant contributor to air pollutant emissions, including nitrogen oxides (NOx) and particulate matter (PM). However, the fuel consumption and pollutant emissions from AM remain poorly quantified in many countries due to a lack of accurate activity data and emissions factors. In this study, the fuel consumption and air pollutant emission from AM were estimated using a survey and emission factors from the literature. A case study was conducted using data collected in Anhui, one of the agricultural provinces of China. The annual active hours of AM in Anhui ranged 130 to 175 h. The estimated diesel fuel consumption by AM was 1.45 Tg in 2013, approximately 25% of the total diesel consumption in the province. The air pollutants emitted by AM were 57 Gg of carbon monoxide, 14 Gg of hydrocarbon, 74 Gg of NOx and 5.7 Gg of PM in 2013. The NOx and PM emissions from AM were equivalent to 17% and 22% of total on-road traffic emissions in Anhui. Among nine types of AM considered, rural vehicles are the largest contributors to fuel consumption (31%) and air emissions (33–45%). [ABSTRACT FROM AUTHOR]