scholarly journals Examining the role of wind in human illness due to pesticide drift in Washington state, 2000–2015

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Edward J. Kasner ◽  
Joanne B. Prado ◽  
Michael G. Yost ◽  
Richard A. Fenske
Keyword(s):  
Wind Speed ◽  
Precision Agriculture ◽  
Meteorological Data ◽  
Weather Conditions ◽  
Complete Information ◽  
Washington State ◽  
Weather Data ◽  
Target Movement ◽  
Pesticide Drift ◽  
Spatio Temporal

Abstract Background Pesticides play an important role in protecting the food supply and the public’s health from pests and diseases. By their nature, pesticides can be toxic to unintended target organisms. Changing winds contribute to pesticide drift— the off-target movement of pesticides—and can result in occupational and bystander illness. Methods We systematically linked historical weather data to documented pesticide drift illnesses. We used Washington State Department of Health data to identify 252 drift events that included 690 confirmed cases of illness from 2000 to 2015. To characterize wind speed and direction at the time of the events, we paired these data with meteorological data from a network of 171 state weather stations. We report descriptive statistics and the spatio-temporal extent of drift events and compare applicator-reported weather conditions to those from nearby meteorological stations. Results Most drift events occurred in tree fruit (151/252 = 60%). Ground spraying and aerial applications accounted for 68% and 23% of events, respectively; 69% of confirmed cases were workers, and 31% were bystanders. Confirmed cases were highest in 2014 (129) from 22 events. Complete applicator spray records were available for 57 drift events (23%). Average applicator-reported wind speeds were about 0.9 m •sec− 1 (2 mi •hr− 1) lower than corresponding speeds from the nearest weather station values. Conclusions Drift events result from a complex array of factors in the agricultural setting. We used known spatio-temporal aspects of drift and historical weather data to characterize these events, but additional research is needed to put our findings into practice. Particularly critical for this analysis is more accurate and complete information about location, time, wind speed, and wind direction. Our findings can be incorporated into new training materials to improve the practice of pesticide application and for better documentation of spray drift events. A precision agriculture approach offers technological solutions that simplify the task of tracking pesticide spraying and weather conditions. Public health investigators will benefit from improved meteorological data and accurate application records. Growers, applicators, and surrounding communities will also benefit from the explanatory and predictive potential of wind ramping studies.

Statistical Analysis of Weather Effects on PM2.5

2012 ◽  
Vol 610-613 ◽  
pp. 1033-1040
Author(s):  
Wei Dai ◽  
Jia Qi Gao ◽  
Bo Wang ◽  
Feng Ouyang
Keyword(s):  
Statistical Analysis ◽  
Wind Speed ◽  
Relative Humidity ◽  
Statistical Methods ◽  
Predictive Analytics ◽  
Hypothesis Test ◽  
Meteorological Data ◽  
Weather Conditions ◽  
Statistical Distribution ◽  
Weather Effects

Effects of weather conditions including temperature, relative humidity, wind speed, wind and direction on PM2.5 were studied using statistical methods. PM2.5 samples were collected during the summer and the winter in a suburb of Shenzhen. Then, correlations, hypothesis test and statistical distribution of PM2.5 and meteorological data were analyzed with IBM SPSS predictive analytics software. Seasonal and daily variations of PM2.5 have been found and these mainly resulted from the weather effects.

Relationship Between Pavement Temperature and Weather Data: Wisconsin Field Study to Verify Superpave Algorithm

1998 ◽  
Vol 1609 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Peter J. Bosscher ◽  
Hussain U. Bahia ◽  
Suwitho Thomas ◽  
Jeffrey S. Russell
Keyword(s):  
Standard Deviation ◽  
Meteorological Data ◽  
Weather Conditions ◽  
Test Site ◽  
Temperature Data ◽  
Pavement Design ◽  
Asphalt Binders ◽  
Weather Data ◽  
Air Temperatures ◽  
Pavement Temperature

Six test sections were constructed on US-53 in Trempealeau County by using different performance-graded asphalt binders to validate the Superpave pavement temperature algorithm and the binder specification limits. Field instrumentation was installed in two of the test sections to monitor the thermal behavior of the pavement as affected by weather. The instrumentation was used specifically to monitor the temperature of the test sections as a function of time and depth from the pavement surface. A meteorological station was assembled at the test site to monitor weather conditions, including air temperature. Details of the instrumentation systems used and analysis of the data collected during the first 22 months of the project are presented. The analysis was focused on development of a statistical model for estimation of low and high pavement temperatures from meteorological data. The model was compared to the Superpave recommended model and to the more recent model recommended by the Long-Term Pavement Performance (LTPP) program. The temperature data analysis indicates a strong agreement between the new model and the LTPP model for the estimation of low pavement design temperature. However, the analysis indicates that the LTPP and Superpave models underestimate the high pavement design temperature at air temperatures higher than 30°C. The temperature data analyses also indicate that there are significant differences between the standard deviation of air temperatures and the standard deviation of the pavement temperatures. These differences raise some questions about the accuracy of the reliability estimates used in the current Superpave recommendations.

scholarly journals FLUKTUASI DEMAM BERDARAH DENGUE TERKAIT VARIABILITAS CUACA DI KLATEN, INDONESIA

2021 ◽  
Vol 13 (1) ◽  
pp. 45-60
Author(s):  
Tri Baskoro Tunggul Satoto ◽  
Nur Alvira Pascawati ◽  
Ajib Diptyanusa ◽  
Luthfan Lazuardi ◽  
Alvin Harjono Dwiputro ◽  
...  
Keyword(s):  
Wind Speed ◽  
Warning System ◽  
Hemorrhagic Fever ◽  
Weather Conditions ◽  
Specific Humidity ◽  
Weather Data ◽  
Dominant Factor ◽  
Regional Patterns ◽  
Central Java ◽  
Preventive Actions

Klaten Regency is one of the Dengue Hemorrhagic Fever (DHF) endemic areas in Central Java. Weather conditions can have an impact on vector dynamics, dengue virus development, and interactions between mosquitoes and humans. The purpose of this study was to determine the pattern of dengue transmission in twenty-six sub-districts in Klaten Regency based on wind speed, specific humidity, rainfall, and temperature. This study was conducted using a retrospective cohort design based on Giovanni-National Aeronautics and Space Administration (NASA) data during the last three years (2016-2018). The independent variables in this study were: wind speed (m/s), specific humidity (g/kg), rainfall (mm/month), and temperature (oC), while the dependent variable was the number of dengue cases in 26 sub-districts in 2014-2014. 2016. Data were analyzed based on monthly patterns and regional patterns using correlation and regression tests with =0.05. The results showed that a total of 1,434 dengue cases were reported during this time period. Weather data analysis revealed that DHF fluctuations were correlated with wind speed in four sub-districts, specific humidity in seven sub-districts, rainfall in three sub-districts, and temperature in three sub-districts. Specific humidity variation plays a role of 21.8% as the dominant factor that can explain the case of DHF in the Klaten Regency. The results of this study can be applied to mitigate the transmission of DHF by determining preventive actions according to place and time and increasing the early warning system to deal with the threat of DHF outbreaks. Abstrak  Kabupaten Klaten adalah salah satu daerah endemis Demam Berdarah Dengue (DBD) di Jawa Tengah. Kondisi cuaca dapat berdampak pada dinamika vektor, perkembangan virus dengue, dan interaksi antara nyamuk dengan manusia. Tujuan dari penelitian ini adalah untuk mengetahui pola penularan DBD di dua puluh enam kecamatan yang berada di Kabupaten Klaten berdasarkan kecepatan angin, kelembaban spesifik, curah hujan dan suhu. Penelitian ini dilakukan menggunakan desain kohort retrospektif berdasarkan pada data Giovanni-National Aeronautics and Space Administration (NASA) selama 3 tahun terakhir (2016-2018). Variabel bebas dalam penelitian ini adalah: kecepatan angin (m/s), kelembaban spesifik (g/kg), curah hujan (mm/bulan) dan suhu (oC), sedangkan variabel terikat adalah jumlah kasus DBD di 26 kecamatan pada tahun 2014-2016. Data dianalisis berdasarkan pola bulanan dan pola wilayah dengan menggunakan uji korelasi dan regresi dengan α=0,05. Hasil penelitian menunjukkan bahwa  total sebanyak 1.434 kasus dengue dilaporkan selama periode waktu tersebut. Analisis data cuaca mengungkapkan bahwa fluktuasi DBD berkorelasi dengan kecepatan angin di empat kecamatan, kelembaban spesifik di tujuh kecamatan, curah hujan di tiga kecamatan dan suhu di tiga kecamatan. Variasi kelembaban spesifik berperan sebesar 21,8% sebagai faktor dominan yang dapat menjelaskan kasus DBD di Kabupaten Klaten.  Hasil studi ini dapat diaplikasikan untuk mitigasi penularan DBD dengan menentukan tidakan pencegahan menurut tempat dan waktu serta meningkatkan sistem kewaspadaan dini untuk menghadapi ancaman KLB DBD.

scholarly journals Design of meteorological information database for forecasting and clustering in microgrid system

2020 ◽  
pp. 55-66
Author(s):  
V. Osypenko ◽  
◽  
N. Kiktev ◽  
T. Lendiel ◽  
◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Information System ◽  
Alternative Energy ◽  
Meteorological Data ◽  
Weather Conditions ◽  
Time Of Day ◽  
Weather Data ◽  
Energy Sources ◽  
Distributed Information System ◽  
Distributed Information

To build Microgrid systems, it is necessary to obtain data from the meteorological service, process them and make decisions about which source of electricity is advisable to use at a given time of day, season, under current weather conditions. The aim of the study is to develop and create a distributed information system database for cluster analysis, processing and storage of incoming meteorological data, a weather forecasting algorithm based on the values of the selected indicators to further determine the type of alternative energy sources used based on the forecast. The article describes designed and implemented distributed information system for reading from the Internet, storing and further processing meteorological data for any region with the aim of forecasting for the effective use of renewable energy sources in Microgrid system. The project is implemented on the basis of a relational database Microsoft SQL Server. Each of the tables has fields that describe the weather conditions necessary to solve the task – to determine the source of electricity, the use of which is cost-effective in a given period of the year, time of day, geographical location and weather conditions. The application that operates with a database has been developed in C # according to the Windows Forms Application template. The distribution of temperature indicators is realized depending on the time of the conducted research for a certain period using cluster analysis. Forecasting weather data is performed using an autoregressive time series model. The user interface was created with Microsoft Visual Studio tools. All data processing is performed on the local server side.

scholarly journals Retrieving and processing agro-meteorological data from API-client sources using R software

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Adrian A. Correndo ◽  
Luiz H. Moro Rosso ◽  
Ignacio A. Ciampitti
Keyword(s):  
Diversity Index ◽  
Meteorological Data ◽  
Weather Data ◽  
State University ◽  
R Software ◽  
Link Type ◽  
Yield Forecast ◽  
Relevant Variables ◽  
Spatio Temporal ◽  
Kansas State University

Abstract Objectives The main purpose of this publication is to help users (students, researchers, farmers, advisors, etc.) of weather data with agronomic purposes (e.g. crop yield forecast) to retrieve and process gridded weather data from different Application Programming Interfaces (API client) sources using R software. Data description This publication consists of a code-tutorial developed in R that is part of the data-curation process from numerous research projects carried out by the Ciampitti’s Lab, Department of Agronomy, Kansas State University. We make use of three weather databases for which specific libraries were developed in R language: (i) DAYMET (Thornton et al. in https://daymet.ornl.gov/, 2019; https://github.com/bluegreen-labs/daymetr), (ii) NASA-POWER (Sparks in J Open Source Softw 3:1035, 2018; https://github.com/ropensci/nasapower), and (iii) Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS) (Funk et al. in Sci Data 2:150066, 2015; https://github.com/ropensci/chirps). The databases offer different weather variables, and vary in terms of spatio-temporal coverage and resolution. The tutorial shows and explain how to retrieve weather data from multiple locations at once using latitude and longitude coordinates. Additionally, it offers the possibility to create relevant variables and summaries that are of agronomic interest such as Shannon Diversity Index (SDI) of precipitation, abundant and well distributed rainfall (AWDR), growing degree days (GDD), crop heat units (CHU), extreme precipitation (EPE) and temperature events (ETE), reference evapotranspiration (ET0), among others.

scholarly journals Low Cost Iot Enable Weather Station for Precision Agriculture

2019 ◽  
Vol 8 (4) ◽  
pp. 3183-3186
Keyword(s):  
Wind Speed ◽  
Wind Direction ◽  
Precision Agriculture ◽  
Low Cost ◽  
Local Level ◽  
Weather Condition ◽  
Weather Conditions ◽  
Mobile App ◽  
Weather Station ◽  
Atmospheric Conditions

Agrometeorology plays an important role in Precision Agriculture for resource management and effects both the quality and quantity of agriculture products. The existing solutions for monitoring weather parameters in agrometeorology are highly global and costly. These solutions are most of the time are inaccessible to the common man or farmers and require frequent physical visits to the field for obtaining information. But in agriculture monitoring highly localized weather condition is required because the weather conditions applicable farm land of one city may not be as such for a farmer of small rural. Weather conditions such as wind speed, wind direction, rainfall, solar radiation, atmospheric pressure, air particle level humidity and temperature measurement plays an important role in different fields like Agriculture, Science, Engineering and Technology. The proposed work provides an optimal solution for monitoring the weather conditions at extremely local level with low cost, compact Internet of Things (IoT) based system. In this paper the design of the system is presented with the use of NodeMCU for realizing the low-cost solution. This low-cost weather station is a product equipped with sensors to measure atmospheric conditions like temperature, humidity, wind speed, wind direction which has predominant effect in agriculture. With embedded IoT connectivity, the proposed weather station is capable to upload the information to IoT cloud ad can be used for further analysis.The user can access the information uploaded by the system anywhere from the world with the help on mobile app or web link on laptop/desktop. The “Low cost Compact IoT enabled Weather Station” does not have any display which make the proposed system more power efficient with overall current rating of about only 80mA to 90mA.

scholarly journals Geophysical and Sedimentological Investigations of Peatlands for the Assessment of Lithology and Subsurface Water Pathways

Geosciences ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 118 ◽  
Author(s):  
Julian Trappe ◽  
Christof Kneisel
Keyword(s):  
Meteorological Data ◽  
Direct Methods ◽  
Weather Conditions ◽  
Bottom Layer ◽  
Subsurface Water ◽  
Mountain Range ◽  
Spatio Temporal ◽  
Multi Method Approach ◽  
Study Sites

Peatlands located on slopes (herein called slope bogs) are typical landscape units in the Hunsrueck, a low mountain range in Southwestern Germany. The pathways of the water feeding the slope bogs have not yet been documented and analyzed. The identification of the different mechanisms allowing these peatlands to originate and survive requires a better understanding of the subsurface lithology and hydrogeology. Hence, we applied a multi-method approach to two case study sites in order to characterize the subsurface lithology and to image the variable spatio-temporal hydrological conditions. The combination of Electrical Resistivity Tomography (ERT) and an ERT-Monitoring and Ground Penetrating Radar (GPR), in conjunction with direct methods and data (borehole drilling and meteorological data), allowed us to gain deeper insights into the subsurface characteristics and dynamics of the peatlands and their catchment area. The precipitation influences the hydrology of the peatlands as well as the interflow in the subsurface. Especially, the geoelectrical monitoring data, in combination with the precipitation and temperature data, indicate that there are several forces driving the hydrology and hydrogeology of the peatlands. While the water content of the uppermost layers changes with the weather conditions, the bottom layer seems to be more stable and changes to a lesser extent. At the selected case study sites, small differences in subsurface properties can have a huge impact on the subsurface hydrogeology and the water paths. Based on the collected data, conceptual models have been deduced for the two case study sites.

Deep Learning Framework for Freeway Speed Prediction in Adverse Weather

2020 ◽  
Vol 2674 (10) ◽  
pp. 28-41
Author(s):  
Abdullah Shabarek ◽  
Steven Chien ◽  
Soubhi Hadri
Keyword(s):  
Deep Learning ◽  
Spatial Data ◽  
Hot Spot ◽  
Weather Conditions ◽  
Absolute Error ◽  
Weather Data ◽  
Adverse Weather ◽  
Speed Prediction ◽  
Traffic Speed ◽  
Spatio Temporal

The introduction of deep learning (DL) models and data analysis may significantly elevate the performance of traffic speed prediction. Adverse weather causes mobility and safety concerns because of varying traffic speeds with poor visibility and road conditions. Most previous modeling approaches have not considered the heterogeneity of temporal and spatial data, such as traffic and weather conditions. This paper presents a framework, consisting of two DL models, to predict traffic speed under normal conditions and during adverse weather, considering prevailing traffic speed, wind speed, traffic volume, road capacity, wind bearing, precipitation intensity, and visibility. To ensure the accuracy of speed prediction, different DL models were assessed. The results indicated that the proposed one-dimensional convolutional neural network model outperformed others in relation to the least root mean square error and the least mean absolute error. Considering real-time weather data feeds on a 15-min basis, a tool was also developed for displaying predicted traffic speeds on New Jersey freeways. Application of the proposed framework models for predicting spatio-temporal hot-spot congestion caused by adverse weather is discussed.

scholarly journals Generation of a Test Reference Year for Alūksne, Latvia

2015 ◽  
Vol 33 (1) ◽  
pp. 46-54 ◽  
Author(s):  
Mārtiņš Ruduks ◽  
Arturs Lešinskis
Keyword(s):  
Selection Process ◽  
Meteorological Data ◽  
Weather Conditions ◽  
Building Energy ◽  
Weather Data ◽  
Building Performance ◽  
Climate Data ◽  
Meteorological Observations ◽  
Energy Simulations

Abstract Precise and reliable meteorological data are necessary for building performance analysis. Since meteorological conditions vary significantly from year to year, there is a need to create a test reference year (TRY), to represent the long-term weather conditions over a year. In this paper two different TRY data models were generated and compared: TRY and TRY-2. Both models where created by analysing every 3-hour weather data for a 30-year period (1984–2013) in Alūksne, Latvia, provided by the Latvian Environment Geology and Meteorology Centre (LEGMC). TRY model was generated according to standard LVS EN ISO 15927-4, but to create second model - TRY-2, 30 year average data were applied. The generated TRY contains typical months from a number of different years. The data gathered from TRY and TRY-2 models where compared with the climate data from the Latvian Cabinet of Ministers regulation No. 379, Regulations Regarding Latvian Building Code LBN 003-01. Average monthly temperature values in LBN 003-01 were lower than the TRY and TRY-2 values. The results of this study may be used in building energy simulations and heating-cooling load calculations for selected region. TRY selection process should include the most recent meteorological observations and should be periodically renewed to reflect the long-term climate change.

scholarly journals Arduino Sensor Integrated Drone for Weather Indices: A Prototype for Pre-flight Preparation

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Theodore Karachalios ◽  
Dimitris Kanellopoulos ◽  
Fotis Lazarinis
Keyword(s):  
Meteorological Data ◽  
Objective Assessment ◽  
Weather Conditions ◽  
Barometric Pressure ◽  
Local Area ◽  
Weather Data ◽  
Map Reading ◽  
Micro Scale ◽  
Arduino Microcontroller ◽  
Local Weather

Commercial weather stations can effectively collect weather data for a specified area. However, their ground sensors limit the amount of data that can be logged, thus failing to collect precise meteorological data in a local area such as a micro-scale region. This happens because weather conditions at a micro-scale region can vary greatly even with small altitude changes. For now, drone operators must check the local weather conditions to ensure a safe and successful flight. This task is often a part of pre-flight preparations. Since flight conditions (and most important flight safety) are greatly affected by weather, drone operators need a more accurate localized weather map reading for the flight area. In this paper, we present the Arduino Sensor Integrated Drone (ASID) with a built-in meteorological station that logs the weather conditions in the vertical area where the drone will be deployed. ASID is an autonomous drone-based system that monitors weather conditions for pre-flight preparation. The operation of the ASID system is based on the Arduino microcontroller running automatic flight profiles to record meteorological data such as temperature, barometric pressure, humidity, etc. The Arduino microcontroller also takes photos of the horizon for an objective assessment of the visibility, the base, and the number of clouds.

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