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Desert steppe soil security issues have been the focus of attention. Therefore, to understand the impact of industrial activities on the soil quality of desert grasslands, this experiment investigated the Gaoshawo Industrial Concentration Zone in Yanchi County. Based on the distance and direction from the industrial park, sample plots were established at intervals of 1–2 km. A total of 82 surface soil samples (0–20 cm) representing different pollution sources were collected. The samples were analysed for pH, total nitrogen, total phosphorus, available phosphorus, available potassium, organic matter, copper (Cu), cadmium (Cd), chromium (Cr), lead (Pb), and zinc (Zn). The desert steppe soil quality was analysed based on the integrated fertility index (IFI) and the Nemerow pollution index (PN), followed by the calculation of the comprehensive soil quality index (SQI), which considers the most suitable soil quality indicators through a geostatistical model. The results showed that the IFI was 0.393, indicating that the soil fertility was relatively poor. Excluding the available potassium, the nugget coefficients of the fertility indicators were less than 25% and showed strong spatial autocorrelation. The average values of Cu, Cd, Cr, Pb and Zn were 21.64 ± 3.26, 0.18 ± 0.02, 44.99 ± 21.23, 87.18 ± 25.84, and 86.63 ± 24.98 mg·kg −1 , respectively; the nugget coefficients of Cr, Pb and Zn were 30.79–47.35%. Pb was the main element causing heavy metal pollution in the study area. Higher PN values were concentrated north of the highway in the study area, resulting in lower soil quality in the northern region and a trend of decreasing soil quality from south to north. The results of this research showed that the average SQI was 0.351 and the soil quality was extremely low. Thus, industrial activities and transportation activities in the Gaoshawo Industrial Zone significantly impact the desert steppe soil quality index.

Soil temperature is a major variable in land surface models, representing soil energy status, storage, and transfer. It serves as an important factor indicating the underlying surface heating condition for weather and climate forecasts. This study utilizes the Weather Research and Forecasting (WRF) model to study the impacts of changes to the surface heating condition, derived from soil temperature observations, on regional weather simulations. Large cold biases are found in the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis project (ERA-40) soil temperatures as compared to observations. At the same time, a warm bias is found in the lower boundary assumption adopted by the Noah land surface model. In six heavy rain cases studied herein, observed soil temperatures are used to initialize the land surface model and to provide a lower boundary condition at the bottom of the model soil layer. By analyzing the impacts from the incorporation of observed soil temperatures, the following major conclusions are drawn: 1) A consistent increase in the ground heat flux is found during the day, when the observed soil temperatures are used to correct the cold bias present in ERA-40. Soil temperature changes introduced at the initial time maintain positive values but gradually decrease in magnitude with time. Sensible and latent heat fluxes and the moisture flux experience an increase during the first 6 h. 2) An increase in soil temperature impacts the air temperature through surface exchange, and near-surface moisture through evaporation. During the first two days, an increase in air temperature is seen across the region from the surface up to about 800 hPa (∼1450 m). The maximum near-surface air temperature increase is found to be, averaged over all cases, 0.5 K on the first day and 0.3 K on the second day. 3) The strength of the low-level jet is affected by the changes described above and also by the consequent changes in horizontal gradients of pressure and thermal fields. Thus, the three-dimensional circulation is affected, in addition to changes seen in the humidity and thermal fields and the locations and intensities of precipitating systems. 4) Overall results indicate that the incorporation of observed soil temperatures introduces a persistent soil heating condition that is favorable to convective development and, consequently, improves the simulation of precipitation.

Particulate matter (PM) has been proven to cause health risks and may result in hospital emergency room visits (ERVs), which might be complicated by extreme temperature events. However, it remains unclear how temperature modulates the effect of different-sized particles on ERVs. This study used three separate time series analyses (2009–2011) to explore such temperature modulation effect in Beijing, China. The analytical approaches included a bivariate response surface model, a non-stratification parametric model, and a stratification parametric model. Results showed that the average daily concentrations of PM 10 and PM 2.5 in Beijing were 110.16 and 67.89 μg/m 3 , respectively, during the study period, which were higher than in most Western countries. Our findings indicated that the temperature modulation effects of PM 2.5 were more evident than that of PM 10 . The effects of PM on morbidity depend on temperature. The effects were estimated for the increases in total, respiratory, and cardiovascular ERVs per 10 μg/m 3 increase in PM 2.5 and PM 10 concentrations at high temperature level (> 28 °C). The estimated increases in the three types of ERVs for PM 2.5 were 0.15, 0.35, and 0.34%, respectively. For PM 10 , the increases were 0.12, 0.08, and 0.14%, respectively. In addition, the results showed that the elderly (age ≥ 65) and women are more vulnerable to PM at high temperatures. These findings may have implications for the health impact associated with both air pollution and global climate change.

Quantitative precipitation forecast (QPF) verification stands out as one of the most formidable endeavors in the realm of forecast verification. Traditional verification methods are not suitable for high-resolution forecasting products in some cases. Therefore, the SAL (structure, amplitude and location) method was proposed as a method of object-based spatial verification that studies precipitation verification in a certain range, which is combined with factors including structure, amplitude and location of the targets. However, the setting of the precipitation threshold would affect the result of the verification. This paper presented an improved method for determining the precipitation threshold using the QPF from ECMWF, which is an ensemble forecast model and three-source fusion product that was used in China from 1 July to 31 August 2020, and then the results obtained with this method were compared with the other two traditional methods. Furthermore, the SAL and the traditional verification methods were carried out for geometric, simulated and real cases, respectively. The results showed the following: (1) The proposed method in this paper for determining the threshold was more accurate at identifying the precipitation objects. (2) The verification area size was critical for SAL calculation. If the area selected was too large, the calculated SAL value had little significance. (3) ME (Mean Error) could not identify the displacement between prediction and observation, while HSS (Heidke Skill Score) was sensitive to the displacement of the prediction field. (4) Compared with the traditional verification methods, the SAL method was more straight forward and simple, and it could give a better representation of prediction ability. Therefore, forecasters can better understand the model prediction effect and what needs to be improved.

Non-planar sensor arrays are used to determine solar orientation based on the orientation matrix formed by orientation vectors of the sensor planes. Solar panels or existing photodiodes can be directly used without increasing the size or mass of the spacecraft. However, a limiting factor for the improvement of the accuracy of orientation lies with the lack of an assessment-based approach. A formulation was developed for the supremum (i.e., the least upper bound) of orientation error of an arbitrary orientation matrix in terms of its influencing factors. The new formulation offers a way to evaluate the supremum of orientation error considering interference with finite energy and interference with infinite energy but finite average energy. For a given non-planar sensor array, a sub-matrix of the full orientation matrix would reach the optimal accuracy of orientation if its supremum of orientation error is the least. Principles for designing an optimal sensor array relate to the configuration of the orientation matrix, which can be pre-determined for a given number of sensors. Simulations and field experiment tested and validated the methods, showing that our sensor array optimization method outperforms the existing methods, while providing a way of assessment and optimization.

The bright band, a layer of enhanced radar reflectivity associated with melting ice particles, is a major source of significant overestimation in quantitative precipitation estimation (QPE) based on the Z–R (reflectivity factor–rain rate) relationship. The effects of the bright band on radar-based QPE can be eliminated by vertical profile of reflectivity (VPR) correction. In this study, we applied bright-band correction algorithms to evaluate three different bands (S-, C- and X-band) of dual-polarized radars and to reduce overestimation errors in Z–R relationship–based QPEs. After the reflectivity was corrected by the algorithms using average VPR (AVPR) alone and a combination of average VPR and the vertical profile of the copolar correlation coefficient (AVPR+CC), the QPEs were derived. The bright-band correction and resulting QPEs were evaluated in eight precipitation events by comparing to the uncorrected reflectivity and rain-gauge observations, separately. The overestimation of Z–R relationship–based QPEs associated with the bright band was reduced after correction by the two schemes for which hourly rainfall was less than 5 mm. For the verification metrics of RMSE (root-mean-square error), RMAE (relative mean absolute error) and RMB (relative mean bias) of QPEs, averaged over all eight cases, the AVPR method improved from 2.28, 0.94 and 0.78 to 1.55, 0.60 and 0.40, respectively, while the AVPR+CC method improved to 1.44, 0.55 and 0.30, respectively. The QPEs after AVPR+CC correction had less overestimation than those after AVPR correction, and similar conclusions were drawn for all three different bands of dual-polarized radars.

China’s desert steppe is the transition zone between the grasslands in central China and the arid desert. Ecological security in this region has long been a subject of debate, both in the local and academic communities. Heavy metals and other pollutants are readily released during industrial production, combustion, and transportation, aggravating the vulnerability of the desert steppes. To understand the impact of industrial activiteis on the heavy metal content of dust fall in the desert steppe, a total of 37 dust fall samples were collected over 90 days. An inductively-coupled plasma mass spectrometer (NexION 350X) was used to measure the concentration of heavy metals Cu, Cd, Cr, Pb, Mn, Co, and Zn in the dust. Using comprehensive pollution index and multivariate statistical analysis methods, we explored the characteristics and sources of heavy metal pollution. We also quantitatively assessed the carcinogenic risks of heavy metals resulting from dust reduction with the help of health risk assessment models. The heavy metals’ comprehensive pollution index values in the study area’s dust fall were ranked as follows: Zn > Cd > Pb > Mn > Cu > Co > Cr. Among these, Zn, Cd, and Pb were significant pollution factors in the study area, and were affected by industrial production and transportation. The high pollution index was concentrated in the north of the research industrial park and on both sides of a highway. The seven heavy metals’ total non-carcinogenic risk index (HI) values were ranked as follows: Mn > Co > Pb > Zn > Cr > Cu > Cd (only the HI of Mn was greater than one). Excluding Mn, the non-carcinogenic and carcinogenic risk index values of the other six heavy metals were within acceptable ranges. Previous studies have also shown that industrial transportation and production have had a significant impact on the heavy metal content of dust fall in the desert steppe.

Ambient air temperature is a key index that affects human health, including mortality risks of cardiovascular disease (CVD). This study quantitatively investigated CVD mortality burden with respect to various segments across the climatic temperature range. Daily data on average temperature and CVD deaths in nine Chinese cities during 2010–2016 were collected for the study. The association between temperature and city-specific CVD mortality was investigated with a distributed lag nonlinear model across lag 0–21 days for cold temperature and lag 0–2 days for hot temperature, and then pooled the association results in a multivariate meta-analysis to derive the pooled estimates of temperature on CVD mortality at national level. Attributable fractions of CVD mortality to cold- and heat-related (i.e., at temperatures below and above the minimum mortality temperature [MMT]) were calculated. In addition, temperature was further separated at 1 °C intervals of ambient temperature and the attributable fractions of each range were calculated. The results showed that the MMT varied from the 71th to 79th percentiles of temperature in nine Chinese cities, centering at 76th at national level. In total, 16.88% of CVD mortality was attributable to nonoptimal temperature, ranging from 9.73% in Hefei to 24.48% in Nanjing. Cold temperature was responsible for most CVD death burden, with a fraction of 14.62%, compared with 2.26% CVD mortality for heat at overall level. The results of temperature stratification suggested that the highest CVD deaths due to temperature fall within moderate cold. Specifically, the highest attributable fractions were at 7 °C, 7 °C, 8 °C, 4 °C, 5 °C, 4 °C, 4 °C, 5 °C, and 6 °C for Harbin, Changchun, Shenyang, Beijing, Shijiazhuang, Nanjing, Hefei, Shanghai, and Chengdu, respectively. Furthermore, the highest CVD deaths due to temperature were near at the start and end time of heating for five northern cities (Harbin, Changchun, Shenyang, Beijing, and Shijiazhuang). Hence, moderately cold temperature plays a noticeable role in impact mortality. Although moderate cold had a slightly lower relative risk than extreme cold, it occurred on more days than did extreme cold. Finally, the cumulative total counts of CVD deaths caused by moderate cold were the largest. We should pay more attention to the adverse health effects of moderate cold in the future. Additionally, the government and heating departments should slow down the rate of increase (or decrease) temperature at the start (or end) time of heating during moderate time in northern China. The findings have important implications for health promotion and disease prevention strategies of adverse temperatures.

The Global Precipitation Measurement (GPM) mission has generated global precipitation products of improved accuracy and coverage that are promising for advanced hydrological and meteorological studies. This study evaluates three Integrated Multi-satellitE Retrievals for GPM (IMERG) Hourly products, including the Early-, Late-, and Final-run products (IMERG-HE, IMERG-HL, and IMERG-HF, respectively), over Sichuan Basin of China. This highly complex terrain of the steep mountainous region offers further scrutiny on the quality and applicability of the data. The China Meteorological Precipitation Analysis (CMPA) data from January 2016 to December 2018 are used as the reference for the evaluation. Results show that: (1) At grid scale, IMERG-HL and IMERG-HF outperform IMERG-HE in terms of correlation coefficient (CC) and root-mean-square error (RMSE), but IMERG-HL has smaller relative bias (RB) than that of the IMERG-HF (by 21.16%). IMERG-HF presents the highest probability of detection (POD = 0.52) and critical success index (CSI = 0.32), except for high false alarm ratio (FAR) for light precipitation. (2) At regional scale, IMERG-HF outperforms IMERG-HE and IMERG-HL in annual evaluation in all the metrics except for the serious overestimation as shown in RB (20.18%, 3.84%, and 4.97%, respectively). Its accumulative precipitation deviation mainly comes from moderate precipitation events (1–10 mm/h), while better detection capability is seen in light precipitation (<1 mm/h). Seasonally, IMERG-HF performs the best in winter, while IMERG-HL performs the best in the other seasons. (3) IMERG-HF captures the peak precipitation more accurately in all seasons. In reproducing the diurnal cycle, IMERG-HF performs better in winter, while IMERG-HL performs better in summer and autumn, and IMERG-HE in spring. However, all three products overestimate the early morning precipitation (01:00–08:00 local standard time) of the diurnal cycle in spring, summer, and autumn.

Frequent emergency room (ER) visits occur for acute symptoms of hypertension or related complications in Beijing, China. Among the meteorological variables, atmospheric pressure directly relates to weather conditions and is hardly affected by artificial factors. In the studies of weather conditions and hypertension-related risks, atmospheric pressure is rarely found in literatures. Based on records of ER visit related to hypertension from three major hospitals in Beijing, from 2008 to 2012, the daily maximum (Pmax) and minimum (Pmin) atmospheric pressures were investigated in this study to identify the influences of air pressure on ER visits. Advanced time series models were utilized in quantifying their associations. In addition, major air pollutants and other meteorological variables including temperature, solar duration, and humidity were incorporated in the multivariate models to reveal the independent association between atmospheric pressure and ER visits. The results indicated the following:1. The number of ER visits positively correlates with all atmospheric pressure metrics in both bivariate and partial correlation analyses.2. The estimated relative risk (RR) corresponding to 1 hPa change in Pmax indicated a significant effect (Pmax = 1029 hPa, RR = 1.107, 95% CIs: 1.034-1.185) from high Pmax on short lag terms. RR for males and the elders grew prominently at lag 1 d, whereas females responded to Pmax at lag 4 d.3. The middle-aged group (45–65 years) was threatened by extremely low Pmin with one day lag.4. As for young (< 45 years) hypertensive patients, no effect from atmospheric pressure was identified.This study may provide new evidence that extreme atmospheric pressure is an independent inducer of hypertension-related symptoms or complications.