Associations Between Ambient Particulate Air Pollution and Cognitive Function in Indonesian Children Living in Forest Fire–Prone Provinces

2021 ◽  
pp. 101053952110317
Author(s):  
Bin Jalaludin ◽  
Frances L. Garden ◽  
Agata Chrzanowska ◽  
Budi Haryanto ◽  
Christine T. Cowie ◽  
...  

Smoke from forest fires can reach hazardous levels for extended periods of time. We aimed to determine if there is an association between particulate matter ≤2.5 µm in aerodynamic diameter (PM2.5) and living in a forest fire–prone province and cognitive function. We used data from the Indonesian Family and Life Survey. Cognitive function was assessed by the Ravens Colored Progressive Matrices (RCPM). We used regression models to estimate associations between PM2.5 and living in a forest fire–prone province and cognitive function. In multivariable models, we found very small positive relationships between PM2.5 levels and RCPM scores (PM2.5 level at year of survey: β = 0.1%; 95% confidence interval [CI] = 0.01% to 0.19%). There were no differences in RCPM scores for children living in forest fire–prone provinces compared with children living in non-forest fire–prone provinces (mean difference = −1.16%, 95% CI = −2.53% to 0.21%). RCPM scores were lower for children who had lived in a forest fire–prone province all their lives compared with children who lived in a non-forest fire–prone province all their life (β = −1.50%; 95% CI = −2.94% to −0.07%). Living in a forest fire–prone province for a prolonged period of time negatively affected cognitive scores after adjusting for individual factors.

2017 ◽  
Vol 86 (1) ◽  
pp. 22-23
Author(s):  
Josiah Marquis ◽  
Meriem Benlamri ◽  
Elizabeth Dent ◽  
Tharmitha Suyeshkumar

Almost half of the Canadian landscape is made up of forests, but the amount of forest surface area burned every year has been growing steadily since 1960.1 This can be problematic due to the effects that forest fires have not only on the local environment but also on the globe as a whole. A forest fire or vegetation fire is defined as any open fire of vegetation such as savannah, forest, agriculture, or peat that is initiated by humans or nature.2 Vegetation fires contribute heavily to air pollution and climate change and are in turn exacerbated by them as well. Air pollution increases due to emissions from these fires, which contain 90-95% carbon dioxide and carbon monoxide as well as methane and other volatile compounds.2 Emissions from forest fires also contribute to global greenhouse gases and aerosol particles (biomass burning organic aerosols),2 leading to indirect and direct consequences to human health. In contrast to biomass burning for household heating and cooking, catastrophic events of forest fires and sweeping grassland fires result in unique exposures and health consequences. In this case report, the relationship between environmental hazardous air pollutants and the potential physiological and psychological health effects associated with the forest fire that affected Fort McMurray, AB in May 2016 are considered.


2014 ◽  
Vol 931-932 ◽  
pp. 1154-1162
Author(s):  
Panuphan Limthavorn ◽  
Watcharapong Tachajapong

At present, the forest fires cause smoke and pollution problem in Chiang Mai that affects human, animal, and ecosystems. Over the past few years, air pollution from forest fire has increased. The resident in northern of Thailand faces problems with air pollution and deforestation. Protecting the forest fires and environmental is the one of top priority in solving this problem in northern of Thailand. Therefore, early burning is one of the solution that has been chosen to used in Chiang Mai forest fire prevention strategies.


2021 ◽  
Vol 192 ◽  
pp. 110298 ◽  
Author(s):  
Barbara A. Maher ◽  
Vincent O'Sullivan ◽  
Joanne Feeney ◽  
Tomasz Gonet ◽  
Rose Anne Kenny

Author(s):  
Meng-Chieh Chen ◽  
Chen-Feng Wang ◽  
Bo-Cheng Lai ◽  
Sun-Wung Hsieh ◽  
Szu-Chia Chen ◽  
...  

The issue of air pollution is gaining increasing attention worldwide, and mounting evidence has shown an association between air pollution and cognitive decline. The aim of this study was to investigate the relationships between air pollutants and cognitive impairment using the Mini-Mental State Exam (MMSE) and its sub-domains. In this study, we used data from the Taiwan Biobank combined with detailed daily data on air pollution. Cognitive function was assessed using the MMSE and its five subgroups of cognitive functioning. After multivariable linear regression analysis, a high level of particulate matter with an aerodynamic diameter of ≤2.5 μm (PM2.5), low ozone (O3), high carbon monoxide (CO), high sulfur dioxide (SO2), high nitric oxide (NO), high nitrogen dioxide (NO2), and high nitrogen oxide (NOx) were significantly associated with low total MMSE scores. Further, high SO2 and low O3 were significantly associated with low MMSE G1 scores. Low O3, high CO, high SO2, high NO2, and high NOx were significantly associated with low MMSE G4 scores, and high PM2.5, high particulate matter with an aerodynamic diameter of ≤10 μm (PM10), high SO2, high NO2, and high NOx were significantly associated with low MMSE G5 scores. Our results showed that exposure to different air pollutants may lead to general cognitive decline and impairment of specific domains of cognitive functioning, and O3 may be a protective factor. These findings may be helpful in the development of policies regarding the regulation of air pollution.


2000 ◽  
Vol 12 (sup3) ◽  
pp. 233-244 ◽  
Author(s):  
W. MacNee, X. Y. Li, P. Gilmour, K. Do

Forests ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 768
Author(s):  
Jin Pan ◽  
Xiaoming Ou ◽  
Liang Xu

Forest fires are serious disasters that affect countries all over the world. With the progress of image processing, numerous image-based surveillance systems for fires have been installed in forests. The rapid and accurate detection and grading of fire smoke can provide useful information, which helps humans to quickly control and reduce forest losses. Currently, convolutional neural networks (CNN) have yielded excellent performance in image recognition. Previous studies mostly paid attention to CNN-based image classification for fire detection. However, the research of CNN-based region detection and grading of fire is extremely scarce due to a challenging task which locates and segments fire regions using image-level annotations instead of inaccessible pixel-level labels. This paper presents a novel collaborative region detection and grading framework for fire smoke using a weakly supervised fine segmentation and a lightweight Faster R-CNN. The multi-task framework can simultaneously implement the early-stage alarm, region detection, classification, and grading of fire smoke. To provide an accurate segmentation on image-level, we propose the weakly supervised fine segmentation method, which consists of a segmentation network and a decision network. We aggregate image-level information, instead of expensive pixel-level labels, from all training images into the segmentation network, which simultaneously locates and segments fire smoke regions. To train the segmentation network using only image-level annotations, we propose a two-stage weakly supervised learning strategy, in which a novel weakly supervised loss is proposed to roughly detect the region of fire smoke, and a new region-refining segmentation algorithm is further used to accurately identify this region. The decision network incorporating a residual spatial attention module is utilized to predict the category of forest fire smoke. To reduce the complexity of the Faster R-CNN, we first introduced a knowledge distillation technique to compress the structure of this model. To grade forest fire smoke, we used a 3-input/1-output fuzzy system to evaluate the severity level. We evaluated the proposed approach using a developed fire smoke dataset, which included five different scenes varying by the fire smoke level. The proposed method exhibited competitive performance compared to state-of-the-art methods.


2021 ◽  
pp. 103052
Author(s):  
Phuong T.M. Tran ◽  
Max G. Adam ◽  
Kwok Wai Tham ◽  
Stefano Schiavon ◽  
Jovan Pantelic ◽  
...  

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