A descriptor for the local dust storm occurrence probability constituted by meteorological factors

Based on daily data sets of 17 meteorological factors during the period of 1954-2005 for 60 gauge stations distributed over Gansu Province of China and the corresponding dust storm records, the dust storm probabilities related to different classes of each factor have been calculated and analyzed. On the basis of statistical analysis, a meteorological descriptor quantifying the daily dust storm occurrence probability for each station, which is referred to as the Dust Storm Occurrence Probability Index （DSOPI）, has been effectively established. According to the statistical characteristics of DSOPI for each station, a feasible judging criterion for a dust storm event has been determined, which can greatly contribute to forecasting dust storms and completing the unavailable historic dust storm records. Meanwhile, the average daily dust storm probability related to each factor on the dust storm day for each station has been specially analyzed in detail, finally disclosing that, in general, the more signifi- cant one factor＇s influence on dust storms, the greater its contribution to them; and each factor＇s contribution clearly varies from place to place. Moreover, on average, maximum and mean wind speeds, maximum-speed wind direction, daily sunny hours, evaporation, mean and lowest relative humidity, lowest surface air pressure and vapor pressure contribute to dust storm events in Gansu Province most greatly in order among all the 17 involved factors.

Predicted Mean Vote of Subway Car Environment Based on Machine Learning

The thermal comfort of passengers in the carriage cannot be ignored.Thus,this research aims to establish a prediction model for the thermal comfort of the internal environment of a subway car and find the optimal input combination in establishing the prediction model of the predicted mean vote(PMV)index.Data-driven modeling utilizes data from experiments and questionnaires conducted in Nanjing Metro.Support vector machine(SVM),decision tree(DT),random forest(RF),and logistic regression(LR)were used to build four models.This research aims to select the most appropriate input variables for the predictive model.All possible combinations of 11 input variables were used to determine the most accurate model,with variable selection for each model comprising 102350 iterations.In the PMV prediction,the RF model was the best when using the correlation coefficients square(R2)as the evaluation indicator(R^(2):0.7680,mean squared error(MSE):0.2868).The variables include clothing temperature(CT),convective heat transfer coefficient between the surface of the human body and the environment(CHTC),black bulb temperature(BBT),and thermal resistance of clothes(TROC).The RF model with MSE as the evaluation index also had the highest accuracy(R^(2):0.7676,MSE:0.2836).The variables include clothing surface area coefficient(CSAC),CT,BBT,and air velocity(AV).The results show that the RF model can efficiently predict the PMV of the subway car environment.

Optimization of nitrogen fertilizer rate under integrated rice management in a hilly area of Southwest China

China has the world’s highest nitrogen(N)application rate,and the lowest N use efficiency(NUE).With the crop yield increasing,serious N pollution is also caused.An in-situ field experiment(2011–2015)was conducted to examine the effects of three N levels,0(i.e.,no fertilizer N addition to soil),120,and 180 kg N ha-1,using integrated rice management(IRM).We investigated rice yield,aboveground N uptake,and soil surface N budget in a hilly region of Southwest China.Compared to traditional rice management(TRM),IRM integrated raised beds,plastic mulch,furrow irrigation,and triangular transplanting,which significantly improved rice grain yield,straw biomass,aboveground N uptake,and NUE.Integrated rice management significantly improved 15N recovery efficiency(by 10%)and significantly reduced the ratio of potential15N loss(by 8%–12%).Among all treatments,the 120 kg N ha-1 level under IRM achieved the highest 15N recovery efficiency(32%)and 15N residual efficiency(29%),with the lowest 15N loss ratio(39%).After rice harvest,the residual N fertilizer did not achieve a full replenishment of soil N consumption,as the replenishing effect was insufficient(ranging from-31 to-49 kg N ha-1).Furthermore,soil surface N budget showed a surplus(69–146 kg N ha-1)under all treatments,and the N surplus was lower under IRM than TRM.These results indicate IRM as a reliable and stable method for high rice yield and high NUE,while exerting a minor risk of N loss.In the hilly area of Southwest China,the optimized N fertilizer application rate under IRM was found to be 100–150 kg N ha-1.

Integrated rice management simultaneously improves rice yield and nitrogen use efficiency in various paddy fields

The hilly area of Southwest China is a typical rice production area which is limited by seasonal droughts and low temperature in the early rice growth period.A field experiment was conducted on three typical paddy fields(low-lying paddy field,medium-elevation paddy field,and upland paddy field)in this region.Nitrogen(N)treatment(180 kg N ha-1 year-1)was compared to a control treatment(0 kg N ha-1 year-1)to evaluate the effects of integrated rice management(IRM)on rice growth,grain yield,and N utilization.Integrated rice management integrated raised beds containing plastic mulch,furrow irrigation,and triangular transplanting.In comparison to traditional rice management(TRM),IRM promoted rice tiller development,with 7–13 more tillers per cluster at the maximum tillering stage and 1–6 more tillers per cluster at the end of tillering stage.Integrated rice management significantly increased the rice aboveground biomass by 34.4%–109.0%in different growth periods and the aboveground N uptake by 25.3%–159.0%.Number of productive tillers significantly increased by 33.0%,resulting in a 33.0%increase in grain yield and 8.0%improvement of N use efficiency(NUE).Grain yields were significantly increased in all three paddy fields assessed,with IRM being the most important factor for grain yield and productive tiller development.Effects of paddy field type and N level on N uptake by aboveground plants were reflected in the rice reproductive growth period,with the effects of IRM more striking due to the dry climate conditions.In conclusion,IRM simultaneously improved rice yield and NUE,presenting a valuable rice management technique in the paddy fields assessed.