Research on Spatial-temporal Variation Characteristics and In fluencing Factors of Annual Maximum Daily Temperature in the Pearl River Basin under the Background of Global Warming by QIN Beilei, WU Xiaoqing, ZHU Tianran, LAI Chengguang

“…This paper analyzes the spatial-temporal variation characteristics of extreme high temperature and quantitatively discusses its influencing factors by the annual maximum daily temperature of 43 meteorological stations in the Pearl River Basin (PRB) from 1960 to 2012 and 74 monthly atmospheric circulation indexes. …”
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A Machine Learning-Based Model for Predicting Atmospheric Corrosion Rate of Carbon Steel by Ngoc-Long Tran, Trong-Ha Nguyen, Van-Tien Phan, Duy-Duan Nguyen

“…Accordingly, seven meteorological and chemical factors of corrosion, namely, the average temperature, the average relative humidity, the total rainfall, the time of wetness, the hours of sunshine, the average chloride ion concentration, and the average sulfur dioxide deposition rate, are used as input variables for the ANN model. …”
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Multi-source data recognition and fusion algorithm based on a two-layer genetic algorithm–back propagation model by Zhuang Xiong, Zhuang Xiong, Jun Ma, Bohang Chen, Haiming Lan, Yong Niu

“…Traditional rainfall data collection mainly relies on rain buckets and meteorological data. It rarely considers the impact of sensor faults on measurement accuracy. …”
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A Practical Approach for Predicting Power in a Small-Scale Off-Grid Photovoltaic System using Machine Learning Algorithms by Aadyasha Patel, O. V. Gnana Swathika, Umashankar Subramaniam, T. Sudhakar Babu, Alok Tripathi, Samriddha Nag, Alagar Karthick, M. Muhibbullah

“…However, solar photovoltaic panel is heavily dependent on meteorological data of the installation site and weather fluctuations. …”
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Development of Optimal Tilt Angle Models of a Photovoltaic Module for Maximum Power Production: Ethiopia by Ahunim Abebe Ashetehe, Belachew Bantyirga Gessesse, Fekadu Shewarega

“…In this paper, generic models were developed that determine the seasonal and annual optimal tilt angle of the Photovoltaic module at any location in Ethiopia without using meteorological data. Both isotropic and anisotropic diffuse solar radiation models were used to estimate monthly, seasonal, and annual optimal tilt angles. …”
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Lessons from COVID-19 and Seoul: Effects of Reduced Human Activity from Social Distancing on Urban CO2 Concentration and Air Quality by Hayoung Park, Sujong Jeong, Ja-Ho Koo, Sojung Sim, Yeon Bae, Yeonsoo Kim, Chaerin Park, Jeongyeon Bang

“…Moreover, the insignificant change in wind speed and wind direction during the months of February and March 2020 compared to 2019 implies that CO2, CO, and NO2 concentrations have not been influenced by meteorological conditions, but mainly by changes in emissions from decreased human activity. …”
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Relações entre o clima e saúde: o caso da dengue no Rio Grande do Sul no período de 2007 a 2017 by Erika Collischonn, Vincent Dubreuil, Francisco de Assis Mendonça

“…The secondary data used for the analysis were: the monthly values of temperature, precipitation and their climatological norm (1961-1990) for 16 meteorological stations (INMET) distributed within the state; confirmed cases of indigenous dengue fever for 19 regional health coordinations per year (SINAN Net-RS); monthly sea surface temperature anomalies according to Niño 3.4 (NOAA / CPC) and ENSO events by year. …”
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Deciphering Abiotic Influences on the Seasonal Dynamics of Leaf Folder and Caseworm Infesting Rice in the Konkan Region of Maharashtra by Nikhil Khemrajji Hatwar, Vinayak Narayan Jalgaonkar, Priti Sunil Shigwan, Kumud Vitthal Naik, Aaditya Jadhav

“…The results showed that the initial detection of rice leaf folders and caseworms occurred during the 31st Standard Meteorological Week (SMW). The highest incidence of rice leaf folders (1.04%) and caseworms (0.69%) was recorded in the 34th SMW. …”
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Internet das coisas (IoT) e novas dinâmicas da produção agrícola no campo brasileiro by Mait Bertollo, Ricardo Abid Castillo, Matheus Dezidério Busca

“…In the agribusiness sector, this system of technical objects such as sensors, equipment and machines, digitally connected through the Internet of Things (Internet of Things or IoT), allows the sharing of large volumes of data of the most varied types with great fluidity, feeding several stages of the production process, such as detection and monitoring of agricultural activities, industrial food processing and meteorological variables predictive analysis. These operations are carried out remotely and automatically by equipment such as smartphones, tablets and computerized agricultural machinery equipped with GPS. …”
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Climate effect on rainfed wheat production in Zarfshan valley with emphasis on Ryan Panjkent by Mohamad hosein hoseini rozbahani

“…The data used in this study were collected through the Tajik Meteorological Department and the Tajik Ministry of Agriculture and the Pentecostal Agricultural Office in the field and in libraries. …”
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Spatio-temporal Distribution Characteristics and Future Trend Analysis of Extreme Climate Indices in Nenjiang River Basin over the Past 60 Years by GONG Xiao, DAI Changlei, LI Shuling, XIAO Yue

“…Based on the observation data such as daily temperature and precipitation from 12 meteorological stations in the Nenjiang River Basin between 1960 and 2020, this paper selects 26 extreme climate indices. …”
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Tephra fallout hazard assessment for a hydrovolcanic eruptive scenario in Mayotte by Audrey Michaud-Dubuy, Jean-Christophe Komorowski, Tristan Lacombe, Lucia Gurioli

“…Using the 2-D tephra dispersal model HAZMAP and the 1979 $$-$$ 2021 meteorological ERA-5 database, we first identify single eruptive scenarios of various impacts for the population of Mayotte. …”
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The role of TCI and TCCI indexes in regional tourism planning by Dobrila Lukić, Marko D. Petrović, Milan M. Radovanović, Tatiana N. Tretiakova, Julia A. Syromiatnikova

“…To achieve it, the climate elements were first analyzed at five meteorological stations in Serbian Podunavlje: Sombor, Novi Sad, Belgrade, Veliko Gradište, and Negotin for the period 1990–2010. …”
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Phytoremediation potential of green spaces in the city by E. Yu. Zaykova, S. S. Feofanova

“…All of them have common long-term goals that coincide with the recommendations of the World Meteorological Organization and the United Nations Office for Disaster Risk Reduction.Materials and methods. …”
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COVID-19 as a Factor Influencing Air Quality? A City Study in China by Chengming Li, Hui Shang, Zhihao Cui, Zhaoxin Dai, Zhaoting Ma

“…We also assessed the influence of meteorological factors and industrial exhaust emissions. …”
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Spatial-Temporal Evolution Characteristics and Influencing Factors of Vegetation NDVI in Hebei Province by LIAN Xi, CHENG Yao, YUAN Jidong, YUAN Zeshen

“…In order to explore the vegetation growth status and influencing factors,Hebei Province is taken as the research area.Based on satellite remote sensing data and meteorological data,the Mann-Kendall trend test,Sen slope,and partial correlation analysis are used to explore the temporal and spatial distribution characteristics of vegetation in Hebei Province,and the influencing factors of vegetation NDVI change in Hebei Province are analyzed by combining the correlation among NDVI,climatic factors,and human activities.The results show that ① the NDVI value of vegetation in Hebei Province from 2001 to 2020 shows an overall growth trend,with an average annual growth rate of 2.84×10^-3.Specifically,it shows a rapid growth trend from 2001 to 2005 and a slow growth trend from 2005 to 2020, with fluctuations.②The NDVI value of vegetation in the study area increases gradually from south to north,and the vegetation improvement area (81.71%) is greater than that in the degraded area (13.79%),and the improvement area is mainly concentrated in Chengde and Zhangjiakou City.③Human activities (GDP) are one of the main influencing factors affecting the growth of NDVI,followed by climatic factors (precipitation and temperature).The change in GDP has a significant impact on vegetation growth,and the improvement of vegetation in Hebei Province is mainly related to forest land and grassland.Among the climatic factors,precipitation is the main influencing factor.…”
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Chiral Pesticides in Soil and Water and Exchange with the Atmosphere by Terry F. Bidleman, Andi D. Leone, Renee L. Falconer, Tom Harner, Liisa M.M. Jantunen, Karin Wiberg, Paul A. Helm, Miriam L. Diamond, Binh Loo

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Short-term effects of combined environmental factors on respiratory disease mortality in Qingdao city: A time-series investigation. by Xin Zhang, Zijian Xi, Min Yang, Xiuqin Zhang, Ruikai Wu, Shuang Li, Lu Pan, Yuan Fang, Peng Lv, Yan Ma, Haiping Duan, Bingling Wang, Kunzheng Lv

“…<h4>Background</h4>It is crucial to comprehend the interplay between air pollution and meteorological conditions in relation to population health within the framework of "dual-carbon" targets. …”
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Integration and Comparative Analysis of Remote Sensing and In Situ Observations of Aerosol Optical Characteristics Beneath Clouds by Jing Chen, Jing Duan, Ling Yang, Yong Chen, Lijun Guo, Juan Cai

“…Lu reveals the following: (1) The AOD seasonal distribution frequencies under both clear-sky and cloudy-sky conditions are unimodal, with a values of 0.2∼0.6, and the inhomogeneity of the aerosol distribution in winter is evident; the seasonal difference in the AOD under clear-sky conditions is more significant, following the order of spring > summer > winter > autumn, and the AOD seasonal difference under cloudy-sky conditions is not obvious. (2) In the analysis of the AOD monthly variations, due to the influence of the meteorological conditions (high humidity, low wind speed) and pollutant transport, the AOD reached its peak in February (clear-sky: 0.63, cloudy-sky: 0.82). (3) Under clear-sky conditions, the negative correlation between the daily variations in AOD, and visibility is more significant during the daytime, and after 12:00, the AOD is positively correlated with <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>P</mi><msub><mi>M</mi><mrow><mn>2.5</mn></mrow></msub></mrow></semantics></math></inline-formula>; these results indicate that the AOD is affected mainly by pollutants and the boundary layer height. …”
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A Review of the Detection Methods for Climate Regime Shifts by Qunqun Liu, Shiquan Wan, Bin Gu

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