Ecological effects of the microbial weathering of silicate minerals

1 Introduction Global climate change is one of the greatest challenges facing humankind in the 21st century.Studying,and utilising,the carbon sink caused by the weathering of silicate minerals has been a key research focus for

Evaluations and Suggestions about Effect of Weather in Guangxi on Rice Production

[Objective] The paper aims to study the effect of weather on rice production in Guangxi. [Method] The authors evaluated the effect of weather and weather disasters in Guangxi on rice production by comparison and analysis in terms of temperature,rain and sunlight in 2009. [Result] The study summarized the main favorable and unfavorable weather conditions of rice growth,and proposed the measures and suggestions to tend to interest and avoid harm on rice production in Guangxi. [Conclusion] This study provides references to the evaluations about effect of weather in Guangxi on rice production and suggestions on how to reduce weather disasters influence and ensure rice production security.

Effects of irrigation on precipitation in the arid regions of Xinjiang,China

Soil moisture is an important parameter for the interaction between soil and atmosphere. It is the sec- ond important factor that influences climate change, next to sea surface temperature （SST）. Most previous studies focused on the monsoon regions in East China, and only a few laid emphases on arid environments. In Xinjiang, which is located in Northwest China, the climate is typically arid and semi-arid. During the past 20 years, the pre- cipitation in Xinjiang has shown a significant increasing trend, and it is closely related to oasis irrigation. This paper aims at discussing whether abnormal soil moisture in spring can be the signal to forecast summer precipitation. The effects of abnormal soil moisture due to farm irrigation in spring in arid environments on regional climate are inves- tigated by using a regional climate model （RegCM3）. The results indicate that positive soil moisture anomaly in irrigated cropland surface in May led to an increase in precipitation in spring as well as across the whole summer. The impact could last for about four months. The effects of soil moisture on the surface air temperature showed a time-lagging trend. The summer air temperature declined by a maximum amplitude of 0.8℃. The increased soil moisture could enhance evaporation and ascending motion in the low troposphere, which brought in more precipi- tation. The soil moisture affected regional weather and climate mainly by altering the surface sensible and latent heat fluxes.

The Weather Temperature and Air Pollution Interaction and Its Effect on Hospital Admissions due to Respiratory System Diseases in Western China

Air pollution has ever become a global major public health problem.Previous studies showed that air pollution is associated with excessive mortality and morbidity of respiratory disease[1-2].The extreme weather temperature can impact human health and the thermal stresses can lead not only to direct deaths and illnesses,but also to aggravation of respiratory disease[3-4].Though the independent

Effect of Weather on the Spread of COVID-19 Using Eigenspace Decomposition

Since the end of 2019,the world has suffered from a pandemic of the disease called COVID-19.WHO reports show approximately 113M confirmed cases of infection and 2.5 M deaths.All nations are affected by this nightmare that continues to spread.Widespread fear of this pandemic arose not only from the speed of its transmission:a rapidly changing“normal life”became a fear for everyone.Studies have mainly focused on the spread of the virus,which showed a relative decrease in high temperature,low humidity,and other environmental conditions.Therefore,this study targets the effect of weather in considering the spread of the novel coronavirus SARS-CoV-2 for some confirmed cases in Iraq.The eigenspace decomposition technique was used to analyze the effect of weather conditions on the spread of the disease.Our theoretical findings showed that the average number of confirmed COVID-19 cases has cyclic trends related to temperature,humidity,wind speed,and pressure.We supposed that the dynamic spread of COVID-19 exists at a temperature of 130 F.The minimum transmission is at 120 F,while steady behavior occurs at 160 F.On the other hand,during the spread of COVID-19,an increase in the rate of infection was seen at 125%humidity,where the minimum spread was achieved at 200%.Furthermore,wind speed showed the most significant effect on the spread of the virus.The spread decreases with a wind speed of 45 KPH,while an increase in the infectious spread appears at 50 KPH.

High-resolution peak demand estimation using generalized additive models and deep neural networks

This paper covers predicting high-resolution electricity peak demand features given lower-resolution data.This is a relevant setup as it answers whether limited higher-resolution monitoring helps to estimate future high-resolution peak loads when the high-resolution data is no longer available.That question is particularly interesting for network operators considering replacing high-resolution monitoring by predictive models due to economic considerations.We propose models to predict half-hourly minima and maxima of high-resolution(every minute)electricity load data while model inputs are of a lower resolution(30 min).We combine predictions of generalized additive models(GAM)and deep artificial neural networks(DNN),which are popular in load forecasting.We extensively analyze the prediction models,including the input parameters’importance,focusing on load,weather,and seasonal effects.The proposed method won a data competition organized by Western Power Distribution,a British distribution network operator.In addition,we provide a rigorous evaluation study that goes beyond the competition frame to analyze the models’robustness.The results show that the proposed methods are superior to the competition benchmark concerning the out-of-sample root mean squared error(RMSE).This holds regarding the competition month and the supplementary evaluation study,which covers an additional eleven months.Overall,our proposed model combination reduces the out-of-sample RMSE by 57.4%compared to the benchmark.

Community Dynamics of Seed Rain in Mixed Evergreen Broad-leaved and Deciduous Forests in a Subtropical Mountain of Central China

Seed dispersal is a key process within community dynamics. The spatial and temporal variations of seed dispersal and the interspecific differences are crucial for understanding species coexistence and community dynamics. This might also hold for the mixed evergreen broadleaved and deciduous forests in the mountains of subtropical China, but until now little existing knowledge is available for this question. In 2001, we chose to monitor the seed rain process of our mixed evergreen broad-leaved and deciduous forest communities in Mount Dalaoling National Forest Park, Yichang, Hubei Province, China. The preliminary analyses show obvious variations in seed rain density, species compositions and timing of seed rain among four communities. The average seed rain densities of the four communities are 2.43 ± 5.15, 54.13 ±182.75, 10.05 ±19.30 and 24.91 ± 58.86 inds/m^2, respectively; about one tenth the values in other studies in subtropical forests of China. in each community, the seed production is dominated by a limited number of species, and the contributions from the others are generally minor. Fecundity of evergreen broadleaved tree species is weaker than deciduous species. The seed rain of four communities begins earlier than September, and stops before December, peaking from early September to late October. The beginning date, ending date and peak times of seed rain are extensively varied among the species, indicating different types of dispersal strategies. According to the existing data, the timing of seed rain is not determined by the climate conditions in the same period, while the density of seed rain may be affected by the disturbances of weather variations at a finer temporal resolution.

Characterizing the Winter Concurrent Variation Patterns of the Subtropical and Polar-Front Jets over East Asia

In this study, the concurrent variation relationships between the East Asian subtropical jet （EASJ） and polar-front jet （PFJ） over the East Asian land mass in the winter season on different timescales are identified and the impacts of the jet concurrent variation patterns on the atmospheric circulation in mid-high latitude regions and climate ano- malies in China are examined, using NCEP-NCAR reanalysis data and observational data. The major variability modes of the winter upper-level wind field on interannual timescales are characterized by the meridional shift of the PFJ and out-of-phase variation in the intensity of the subtropical jet and PFJ. On subseasonal and synoptic timescales, the concurrent variation relationships can be categorized into four configuration patterns： a strong （weak） subtropical jet accompanied by a weak （strong） PFJ, or a strong （weak） subtropical jet with a strong （weak） PFJ. The out-of- phase variation [i.e., a stronger （weaker） EASJ and weaker （stronger） PFJ] is found to be more common than the in- phase variation [i.e., a stronger （weaker） EASJ and stronger （weaker） PF J]. These concurrent variation relationships repre- sent the integral structure and variation features of the atmospheric general circulation over East Asia, and have signi- ficant impacts on the weather and climate. The strong subtropical jet/weak PFJ （weak subtropical jet/strong PFJ） pat- tern leads to anomalous negative （positive） geopotential height in midlatitude regions and favors cold （warm） condi- tions, and positive （negative） rainfall anomalies in southern China. For both strong jet configurations, the geopoten- tial height anomaly in the mid-high latitudes shows a northwest-southeast tilted dipole pattern, resulting in northern warm-southern cold temperature anomalies, and positive rainfall anomalies in southern China. For both weak jet situations, positive geopotential height anomalies dominate the East Asian area, and warm conditions occur over most areas in China, corresponding to less negative rainfall anomalies in southern China. The complicated rainfall and temperature anomaly patterns in China can be explained by the concurrent variation relationships between the two jets. A close relationship may exist between the synoptic-scale transient eddy activity （STEA） and the intensity of jet streams, especially for the PFJ. Significantly reduced （strengthened） STEA over the polar-front area is intim- ately associated with a decreased （increased） intensity of the PFJ.

Weather-based interruption prediction in the smart grid utilizing chronological data

This unique study will demonstrate a combined effect of weather parameters on the total number of power distribution interruptions in a region.Based on common weather conditions,a theoretical model can predict interruptions and risk assessment with immediate weather conditions.Using daily and hourly weather data,the created models will predict the number of daily or by-shift interruptions.The weather and environmental conditions to be addressed will include rain,wind,temperature,lightning density,humidity,barometric pressure,snow and ice.Models will be developed to allow broad applications.Statistical and deterministic simulations of the models using the data collected will be conducted by employing existing software,and the results will be used to refine the models.Models developed in this study will be used to predict power interruptions in areas that can be readily monitored,thus validating the models.The application has resulted in defining the predicted number of interruptions in a region with a specific confidence level.Reliability is major concern for every utility.Prediction and timely action to minimize the outage duration improves reliability.Use of this predictor model with existing smart grid self-healing technology is proposed.

Identifying the Correlation Between Ambient Temperature and Gas Consumption in a Local Energy System

In order to understand energy consumption and ensure precise load prediction,it is essential to identify the variation of gas consumption in response to ambient temperature change outdoor.In this paper,the relationship is identified by using Empirical Mode Decomposition(EMD)and linear regression analysis together with outlier detection.EMD is a data processing tool that can divide original data into several Intrinsic Mode Functions(IMFs)with a lower frequency residue.By applying the data mining technique-Mahalanobis distance measurement,some outliers from real-time gas consumption and temperature data points are detected,which are excluded from the data sets to ensure accuracy.Correlation coefficients between the gas load and ambient temperature are calculated and denoted as an important index to quantify their relationship through regression analysis.By comparing such indices on realtime data and EMD processed data,the weather-sensitive part of gas demand is identified.The methods are implemented on a local energy system and the results reveal that the outcome after EMD presents a higher level of correlation between the gas load and ambient temperature,compared to the results from directly using the real-time gas load and temperature data.

A new method for estimating the longevity and degradation of hotovoltaic systems considering weather states

The power output of solar photovoltaic （PV） systems is affected by solar radiation and ambient temperature. The commonly used evaluation techniques usually overlook the four weather states which are clear, cloudy, foggy, and rainy. In this paper, an ovel analytical model of the four weather conditions based on the Markov chain is proposed. The Markov method is well suited to estimate the reliability and availability of systems based on a continuous stochastic process. The proposed method is generic enough to be applied to reliability evaluation of PV systems and even other applications. Further aspects investigated include the new degradation model for reliability predication of PV modules. The results indicate that the PV module degradation over years, failures, and solar radiation must be considered in choosing an efficient PV system with an optimal design to achieve the maximum benefit of the PV system. For each aspect, a method is proposed, and the complete focusing methodology is expounded and validated using simulated point targets. The results also demonstrate the feasibility and applic- ability of the proposed method for effective modeling of the chronological aspects and stochastic characteristics of solar cells as well as the optimal configuration and sizing of large PV plants in terms of cost and reliability.