scholarly journals Accurate, Low Cost PM2.5 Measurements Demonstrate the Large Spatial Variation in Wood Smoke Pollution in Regional Australia and Improve Modeling and Estimates of Health Costs

Atmosphere ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 856
Author(s):  
Dorothy L. Robinson

The accuracy and utility of low-cost PM2.5 sensors was evaluated for measuring spatial variation and modeling population exposure to PM2.5 pollution from domestic wood-heating (DWH) in Armidale, a regional town in New South Wales (NSW), Australia, to obtain estimates of health costs and mortality. Eleven ‘PurpleAir’ (PA) monitors were deployed, including five located part of the time at the NSW government station (NSWGov) to derive calibration equations. Calibrated PA PM2.5 were almost identical to the NSWGov tapered element oscillating microbalance (TEOM) and Armidale Regional Council’s 2017 DustTrak measurements. Spatial variation was substantial. National air quality standards were exceeded 32 times from May–August 2018 at NSWGov and 63 times in one residential area. Wood heater use by about 50% of households increased estimated annual PM2.5 exposure by over eight micrograms per cubic meter, suggesting increased mortality of about 10% and health costs of thousands of dollars per wood heater per year. Accurate real-time community-based monitoring can improve estimates of exposure and avoid bias in estimating dose-response relationships. Efforts over the past decade to reduce wood smoke pollution proved ineffective, perhaps partly because some residents do not understand the health impacts or costs of wood-heating. Real-time Internet displays can increase awareness of DWH and bushfire pollution and encourage governments to develop effective policies to protect public health, as recommended by several recent studies in which wood smoke was identified as a major source of health-hazardous air pollution.

Author(s):  
Gabriel de Almeida Souza ◽  
Larissa Barbosa ◽  
Glênio Ramalho ◽  
Alexandre Zuquete Guarato

2007 ◽  
Author(s):  
R. E. Crosbie ◽  
J. J. Zenor ◽  
R. Bednar ◽  
D. Word ◽  
N. G. Hingorani

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Yong He ◽  
Hong Zeng ◽  
Yangyang Fan ◽  
Shuaisheng Ji ◽  
Jianjian Wu

In this paper, we proposed an approach to detect oilseed rape pests based on deep learning, which improves the mean average precision (mAP) to 77.14%; the result increased by 9.7% with the original model. We adopt this model to mobile platform to let every farmer able to use this program, which will diagnose pests in real time and provide suggestions on pest controlling. We designed an oilseed rape pest imaging database with 12 typical oilseed rape pests and compared the performance of five models, SSD w/Inception is chosen as the optimal model. Moreover, for the purpose of the high mAP, we have used data augmentation (DA) and added a dropout layer. The experiments are performed on the Android application we developed, and the result shows that our approach surpasses the original model obviously and is helpful for integrated pest management. This application has improved environmental adaptability, response speed, and accuracy by contrast with the past works and has the advantage of low cost and simple operation, which are suitable for the pest monitoring mission of drones and Internet of Things (IoT).


2021 ◽  
Vol 11 (11) ◽  
pp. 4940
Author(s):  
Jinsoo Kim ◽  
Jeongho Cho

The field of research related to video data has difficulty in extracting not only spatial but also temporal features and human action recognition (HAR) is a representative field of research that applies convolutional neural network (CNN) to video data. The performance for action recognition has improved, but owing to the complexity of the model, some still limitations to operation in real-time persist. Therefore, a lightweight CNN-based single-stream HAR model that can operate in real-time is proposed. The proposed model extracts spatial feature maps by applying CNN to the images that develop the video and uses the frame change rate of sequential images as time information. Spatial feature maps are weighted-averaged by frame change, transformed into spatiotemporal features, and input into multilayer perceptrons, which have a relatively lower complexity than other HAR models; thus, our method has high utility in a single embedded system connected to CCTV. The results of evaluating action recognition accuracy and data processing speed through challenging action recognition benchmark UCF-101 showed higher action recognition accuracy than the HAR model using long short-term memory with a small amount of video frames and confirmed the real-time operational possibility through fast data processing speed. In addition, the performance of the proposed weighted mean-based HAR model was verified by testing it in Jetson NANO to confirm the possibility of using it in low-cost GPU-based embedded systems.


Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 268
Author(s):  
Todd C. Harris ◽  
Laurent Vuilleumier ◽  
Claudine Backes ◽  
Athanasios Nenes ◽  
David Vernez

Epidemiology and public health research relating to solar ultraviolet (UV) exposure usually relies on dosimetry to measure UV doses received by individuals. However, measurement errors affect each dosimetry measurement by unknown amounts, complicating the analysis of such measurements and their relationship to the underlying population exposure and the associated health outcomes. This paper presents a new approach to estimate UV doses without the use of dosimeters. By combining new satellite-derived UV data to account for environmental factors and simulation-based exposure ratio (ER) modelling to account for individual factors, we are able to estimate doses for specific exposure periods. This is a significant step forward for alternative dosimetry techniques which have previously been limited to annual dose estimation. We compare our dose estimates with dosimeter measurements from skiers and builders in Switzerland. The dosimetry measurements are expected to be slightly below the true doses due to a variety of dosimeter-related measurement errors, mostly explaining why our estimates are greater than or equal to the corresponding dosimetry measurements. Our approach holds much promise as a low-cost way to either complement or substitute traditional dosimetry. It can be applied in a research context, but is also fundamentally well-suited to be used as the basis for a dose-estimating mobile app that does not require an external device.


Author(s):  
Cheyma BARKA ◽  
Hanen MESSAOUDI-ABID ◽  
Houda BEN ATTIA SETTHOM ◽  
Afef BENNANI-BEN ABDELGHANI ◽  
Ilhem SLAMA-BELKHODJA ◽  
...  

Author(s):  
Donya Khaledyan ◽  
Abdolah Amirany ◽  
Kian Jafari ◽  
Mohammad Hossein Moaiyeri ◽  
Abolfazl Zargari Khuzani ◽  
...  

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