Study on the Prediction of Heat Index during Cotton Seedtime in North Xinjiang

2008 ◽  
Vol 25 (2) ◽  
pp. 259-265 ◽  
Author(s):  
Wei-yi MAO
Keyword(s):  
2020 ◽  
Vol 82 ◽  
pp. 149-160
Author(s):  
N Kargapolova

Numerical models of the heat index time series and spatio-temporal fields can be used for a variety of purposes, from the study of the dynamics of heat waves to projections of the influence of future climate on humans. To conduct these studies one must have efficient numerical models that successfully reproduce key features of the real weather processes. In this study, 2 numerical stochastic models of the spatio-temporal non-Gaussian field of the average daily heat index (ADHI) are considered. The field is simulated on an irregular grid determined by the location of weather stations. The first model is based on the method of the inverse distribution function. The second model is constructed using the normalization method. Real data collected at weather stations located in southern Russia are used to both determine the input parameters and to verify the proposed models. It is shown that the first model reproduces the properties of the real field of the ADHI more precisely compared to the second one, but the numerical implementation of the first model is significantly more time consuming. In the future, it is intended to transform the models presented to a numerical model of the conditional spatio-temporal field of the ADHI defined on a dense spatio-temporal grid and to use the model constructed for the stochastic forecasting of the heat index.


2010 ◽  
Vol 18 (5) ◽  
pp. 497
Author(s):  
Li Lei ◽  
Liu Tong ◽  
Liu Bin ◽  
Liu Zhongquan ◽  
Si Langming ◽  
...  

2021 ◽  
pp. 1420326X2199462
Author(s):  
Stefano Ridolfi ◽  
Susanna Crescenzi ◽  
Fabiana Zeli ◽  
Stefano Perilli ◽  
Stefano Sfarra

This work is centred on an ancient Italian church. Since 2011, a restoration plan has been undertaken by following sequential phases. The methodological approach to restoration was guided by environmental monitoring campaigns. In particular, two thermo-hygrometric campaigns were carried out during the warm months of the years 2015 and 2016. The first set of measurements was executed during the restoration of facades and roofs, making it possible to reach even areas that are usually difficult to access. The second set was performed to evaluate the indoor thermo-hygrometric conditions following the work of the previous year. This was intended to assess their differences in variability, the influence of the outdoor environment and any real and perceived improvement. Results demonstrate that thermal images helped in identifying both the heat sources causing thermal discomforts and the good thermal capacity of masonries. Concerning the heat index (HI), the church showed an improvement in the trend of malaise perceived by people during the second summer period (∼2°C lower than 2015). Finally, in the last microclimate monitoring, the roof structure no longer acted as an amplifier for daily temperature excursions.


2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Kevin Lanza ◽  
Melody Alcazar ◽  
Deanna M. Hoelscher ◽  
Harold W. Kohl

Abstract Background Latinx children in the United States are at high risk for nature-deficit disorder, heat-related illness, and physical inactivity. We developed the Green Schoolyards Project to investigate how green features—trees, gardens, and nature trails—in school parks impact heat index (i.e., air temperature and relative humidity) within parks, and physical activity levels and socioemotional well-being of these children. Herein, we present novel methods for a) observing children’s interaction with green features and b) measuring heat index and children’s behaviors in a natural setting, and a selection of baseline results. Methods During two September weeks (high temperature) and one November week (moderate temperature) in 2019, we examined three joint-use elementary school parks in Central Texas, United States, serving predominantly low-income Latinx families. To develop thermal profiles for each park, we installed 10 air temperature/relative humidity sensors per park, selecting sites based on land cover, land use, and even spatial coverage. We measured green features within a geographic information system. In a cross-sectional study, we used an adapted version of System for Observing Play and Recreation in Communities (SOPARC) to assess children’s physical activity levels and interactions with green features. In a cohort study, we equipped 30 3rd and 30 4th grade students per school during recess with accelerometers and Global Positioning System devices, and surveyed these students regarding their connection to nature. Baseline analyses included inverse distance weighting for thermal profiles and summing observed counts of children interacting with trees. Results In September 2019, average daily heat index ranged 2.0 °F among park sites, and maximum daily heat index ranged from 103.4 °F (air temperature = 33.8 °C; relative humidity = 55.2%) under tree canopy to 114.1 °F (air temperature = 37.9 °C; relative humidity = 45.2%) on an unshaded playground. 10.8% more girls and 25.4% more boys interacted with trees in September than in November. Conclusions We found extreme heat conditions at select sites within parks, and children positioning themselves under trees during periods of high heat index. These methods can be used by public health researchers and practitioners to inform the redesign of greenspaces in the face of climate change and health inequities.


Author(s):  
Suwei Wang ◽  
Connor Y. H. Wu ◽  
Molly B. Richardson ◽  
Benjamin F. Zaitchik ◽  
Julia M. Gohlke
Keyword(s):  

2021 ◽  
Author(s):  
Landon B. Lempke ◽  
◽  
Robert C. Lynall ◽  
Rachel K. Le ◽  
Michael McCrea ◽  
...  
Keyword(s):  

Author(s):  
Daniel Samano ◽  
Shubhayu Saha ◽  
Taylor Corbin Kot ◽  
JoNell E. Potter ◽  
Lunthita M. Duthely

Extreme weather events (EWE) are expected to increase as climate change intensifies, leaving coastal regions exposed to higher risks. South Florida has the highest HIV infection rate in the United States, and disruptions in clinic utilization due to extreme weather conditions could affect adherence to treatment and increase community transmission. The objective of this study was to identify the association between EWE and HIV-clinic attendance rates at a large academic medical system serving the Miami-Dade communities. The following methods were utilized: (1) Extreme heat index (EHI) and extreme precipitation (EP) were identified using daily observations from 1990–2019 that were collected at the Miami International Airport weather station located 3.6 miles from the studied HIV clinics. Data on hurricanes, coastal storms and flooding were collected from the National Oceanic and Atmospheric Administration Storms Database (NOAA) for Miami-Dade County. (2) An all-HIV clinic registry identified scheduled daily visits during the study period (hurricane seasons from 2017–2019). (3) Daily weather data were linked to the all-HIV clinic registry, where patients’ ‘no-show’ status was the variable of interest. (4) A time-stratified, case crossover model was used to estimate the relative risk of no-show on days with a high heat index, precipitation, and/or an extreme natural event. A total of 26,444 scheduled visits were analyzed during the 383-day study period. A steady increase in the relative risk of ‘no-show’ was observed in successive categories, with a 14% increase observed on days when the heat index was extreme compared to days with a relatively low EHI, 13% on days with EP compared to days with no EP, and 10% higher on days with a reported extreme weather event compared to days without such incident. This study represents a novel approach to improving local understanding of the impacts of EWE on the HIV-population’s utilization of healthcare, particularly when the frequency and intensity of EWE is expected to increase and disproportionately affect vulnerable populations. More studies are needed to understand the impact of EWE on routine outpatient settings.


2021 ◽  
Vol 10 (3) ◽  
pp. 161
Author(s):  
Hao-xuan Chen ◽  
Fei Tao ◽  
Pei-long Ma ◽  
Li-na Gao ◽  
Tong Zhou

Spatial analysis is an important means of mining floating car trajectory information, and clustering method and density analysis are common methods among them. The choice of the clustering method affects the accuracy and time efficiency of the analysis results. Therefore, clarifying the principles and characteristics of each method is the primary prerequisite for problem solving. Taking four representative spatial analysis methods—KMeans, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Clustering by Fast Search and Find of Density Peaks (CFSFDP), and Kernel Density Estimation (KDE)—as examples, combined with the hotspot spatiotemporal mining problem of taxi trajectory, through quantitative analysis and experimental verification, it is found that DBSCAN and KDE algorithms have strong hotspot discovery capabilities, but the heat regions’ shape of DBSCAN is found to be relatively more robust. DBSCAN and CFSFDP can achieve high spatial accuracy in calculating the entrance and exit position of a Point of Interest (POI). KDE and DBSCAN are more suitable for the classification of heat index. When the dataset scale is similar, KMeans has the highest operating efficiency, while CFSFDP and KDE are inferior. This paper resolves to a certain extent the lack of scientific basis for selecting spatial analysis methods in current research. The conclusions drawn in this paper can provide technical support and act as a reference for the selection of methods to solve the taxi trajectory mining problem.


2017 ◽  
Vol 41 ◽  
pp. 924-930 ◽  
Author(s):  
Zang SEN ◽  
Cao QING ◽  
Alimujiang KEREMU ◽  
Liu SHANHUI ◽  
Zhang YONGJUN ◽  
...  

2021 ◽  
Author(s):  
K. Lova Raju ◽  
V. Vijayaraghavan

Abstract Internet of Things (IoT) based automation has provided sophisticated research and developments in the field of agriculture. In agriculture field production, using environmental and deployment sensors like DHT11, soil moisture, soil temperature, and so on, IoT has been utilised to monitor field conditions and automation in precision agriculture. The environmental parameters, field evaluation, deployment parameters, and shortage of water has become an unresolved task for agriculture monitoring. All of this leads to insufficient production of the agricultural crop. To eradicate the above-mentioned problems, we proposed a system in the using an architectural manner. This system uses an NRF24L01 module with in-built power and low noise amplifiers to enable a long-distance communication for transmission of the field information about the current crop situation to the farmers. This work is investigating an appropriate, reasonable, and applied IoT technology for precision agriculture by considering various applications of agriculture and experiments. The proposed system reduces power consumption, and improves operational efficiency. The proposed system reduces human efforts and also evaluates heat index measurement to monitor the environment. Based on the experiments, the current consumption and life expectancy of the AWMU are determined to be 0.02819 A and 3 days 20 hours 13 minutes and 47 seconds, respectively. Furthermore, the maximum transmission of AWMU is in an environmental location is 200 meters line of sight from the router.


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