Assessing the impact of PM2.5 on respiratory disease using artificial neural networks

Polezer, Gabriela; Tadano, Yara S.; Siqueira, Hugo Valadares; Godoi, Ana F. L.; Yamamoto, Carlos Itsuo; André, Paulo Afonso de; Pauliquevis, Theotônio; Andrade, María de Fátima; Oliveira, Andrea; Saldiva, Paulo Hilário Nascimento; Taylor, Philip; Godoi, Ricardo H. M.

doi:10.1016/j.envpol.2017.12.111

Cited by 122 publications

(90 citation statements)

References 37 publications

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“…PMs consist of a complex mixture of small inorganic and organic PMs originating from various sources . The smaller the PMs, the more harmful they become as they can easily penetrate into the human respiratory system that comprises biological tissues and cells, whose sizes are comparable to that of PM 2.5 , i.e., PMs of size less than 2.5 µm . Thus, it is crucial to develop a facile and efficient method to capture PM 2.5 .…”

Section: Electrospinning Conditions For Nylon‐6 and Nylon‐11 Nanofibementioning

confidence: 99%

Light‐Permeable Air Filter with Self‐Polarized Nylon‐11 Nanofibers for Enhanced Trapping of Particulate Matters

Park

Kim

Lee

et al. 2019

Adv Materials Inter

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Recently, there is a growing demand of high‐performance, lightweight, and light‐permeable air filters due to severe air pollution. Nanofiber air filters have been actively investigated to filter out ultrafine particulate matters (PMs) in air. Among various polymer nanofibers, Nylon‐6 nanofibers have been widely adopted to fabricate air filters owing to their mechanical strength and chemical stability. However, the filtering efficiency of the Nylon‐6 air filter is relatively low when it has high light transmittance. Thus, a relatively thick nanofiber layer is required to ensure the filtering efficiency, leading to a high pressure drop and low light transmittance. Herein, the fabrication and performance of an air filter made of ferroelectric Nylon‐11 nanofibers is introduced. The air filters fabricated with ferroelectric Nylon‐11 demonstrate much improved PM filtering efficiency due to their spontaneous polarization. Specifically, for inorganic PMs, a higher filtration efficiency of ≈70% is obtained for trapping PM2.5 using the filter with 80% light transmittance, which is ≈20% higher compared to that of the Nylon‐6 air filter with the same light transmittance. Particularly, the Nylon‐11 air filter exhibits higher quality factors for both organic and inorganic PMs owing to its high filtering efficiency and breathability. The approach introduced herein is a simple route for fabricating high‐performance air filters.

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Section: Electrospinning Conditions For Nylon‐6 and Nylon‐11 Nanofibementioning

confidence: 99%

Light‐Permeable Air Filter with Self‐Polarized Nylon‐11 Nanofibers for Enhanced Trapping of Particulate Matters

Park

Kim

Lee

et al. 2019

Adv Materials Inter

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“…Since the reform and opening-up policy in 1978, China's total economic output has leaped to the second place globally; at the same time, China has been suffering from serious air pollution, of which haze pollution is of particular concern to the public [5,6]. Since 2013, the complex haze pollution, containing fine particulate matter (PM 2.5 ; ≤ 2.5 µm in aerodynamic diameter) and coarse particulate matter (PM 2.5-10 ; 2.5 µm-10 µm in aerodynamic diameter) as core pollutants [7][8][9], has erupted in large-scale and long-term concentrations in many regions of China. It seriously affects the quality of China's economic Figure 1 shows that the study area consists of 289 cities throughout China, which basically covers all the important cities in China.…”

Section: Introductionmentioning

confidence: 99%

“…VE is the most critical factor that influences PM 2.5 concentrations, which means controlling motor vehicle pollutant emissions is an effective measure to reduce PM 2.5 pollution in Chinese cities.Sustainability 2019, 11, 4751 2 of 17 growth, public health, and government image. Compared with PM 2.5-10 , PM 2.5 has stronger activity, a smaller particle size, and it is easier to carry these harmful substances deep into the respiratory tract, resulting in a significant increase in the risk of cardiac and pulmonary concerns and other related organs [8]. In addition, PM 2.5 has the ability to be transported over long distances and compared with other particles, remains airborne for a greater length of time [10,11].…”

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confidence: 99%

Exploring the Spatial Variation Characteristics and Influencing Factors of PM2.5 Pollution in China: Evidence from 289 Chinese Cities

Zhao

2019

Sustainability

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Haze pollution has become an urgent environmental problem due to its impact on the environment as well as human health. PM2.5 is one of the core pollutants which cause haze pollution in China. Existing studies have rarely taken a comprehensive view of natural environmental conditions and socio-economic factors to figure out the cause and diffusion mechanism of PM2.5 pollution. This paper selected both natural environmental conditions (precipitation (PRE), wind speed (WIN), and terrain relief (TR)) and socio-economic factors (human activity intensity of land surface (HAILS), the secondary industry's proportion (SEC), and the total particulate matter emissions of motor vehicles (VE)) to analyze the effects on the spatial variation of PM2.5 concentrations. Based on the spatial panel data of 289 cities in China in 2015, we used spatial statistical methods to visually describe the spatial distribution characteristics of PM2.5 pollution; secondly, the spatial agglomeration state of PM2.5 pollution was characterized by Moran’s I; finally, several regression models were used to quantitatively analyze the correlation between PM2.5 pollution and the selected explanatory variables. Results from this paper confirm that in 2015, most cities in China suffered from severe PM2.5 pollution, and only 17.6% of the sample cities were up to standard. The spatial agglomeration characteristics of PM2.5 pollution in China were particularly significant in the Beijing–Tianjin–Hebei region. Results from the global regression models suggest that WIN exerts the most significant effects on decreasing PM2.5 concentration (p < 0.01), while VE is the most critical driver of increasing PM2.5 concentration (p < 0.01). Results from the local regression model show reliable evidence that the relation between PM2.5 concentrations and the explanatory variables varied differently over space. VE is the most critical factor that influences PM2.5 concentrations, which means controlling motor vehicle pollutant emissions is an effective measure to reduce PM2.5 pollution in Chinese cities.

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“…Fine particulate matter (particulate matter with an aerodynamic diameter less than or equal to 2.5μm, PM 2.5 ) can increase the risk of occurrence and death of multiple respiratory [1,2], circulatory systemic diseases [3,4]and can increase the morbidity and mortality of tumors [5,6]. In addition, heavy metals carried by PM 2.5 can accumulate in human body and cause chronic hazards.…”

Section: Introductionmentioning

confidence: 99%

PM2.5 concentration forecasting using Long Short-Term Memory Neural Network and Multi-Level Additive Model

Tao¹,

Tian²,

Wu³

et al. 2019

Preprint

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Background PM 2.5 concentration predication can provide an effective way to protect public health by early warning. Though there are many methods available, the comparison between multi-level additive model (AM) and long short-term memory (LSTM) neural network in predicting PM 2.5 concentration is limited. This study aimed to compare the performance of multi-level AM and LSTM in predicting hourly and daily PM 2.5 concentration.Methods Air pollution data from Jul 1, 2016 to Dec 31, 2017 were obtained from Beijing Municipal Environmental Monitoring Center, and meteorological data were derived from the National Meteorological Science Data Sharing Service. Multi-level AM and LSTM were developed to estimate the regional hourly and daily concentration of PM 2.5 .Results In the prediction of hourly PM 2.5 concentrations, LSTM achieved a better performance than multi-level AM (range of R 2 : 0.76-0.92 for LSTM, 0.59-0.78 for multi-level AM; range of root mean square error (RMSE): 6.20-17.58μg/m 3 for LSTM, 19.19-30.81μg/m 3 for multi-level AM; range of mean absolute error (MAE): 4.50-13.42μg/m 3 for LSTM, 13.55-22.35μg/m 3 for multi-level AM; range of mean absolute percentage error (MAPE): 0.18%-0.55% for LSTM, 0.50%-0.87% for multi-level AM). While in the prediction of daily PM 2.5 concentrations, multi-level AM showed a higher predictive accuracy than LSTM (range of R 2 : 0.43-0.93 for LSTM, 0.74-0.98 for multi-level AM; range of RMSE: 32.46-46.82μg/m 3 for LSTM, 4.83-20.98μg/m 3 for multi-level AM; range of MAE: 24.32-34.89μg/m 3 for LSTM, 3.67-16.33μg/m 3 for multi-level AM; range of MAPE: 0.92%-1.74% for LSTM, 0.11%-0.45% for multi-level AM).Conclusion LSTM showed better performance than the multi-level AM when there is a large amount of data, while multi-level AM showed better performance than LSTM when the amount of data is relatively small.

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Assessing the impact of PM2.5 on respiratory disease using artificial neural networks

Cited by 122 publications

References 37 publications

Light‐Permeable Air Filter with Self‐Polarized Nylon‐11 Nanofibers for Enhanced Trapping of Particulate Matters

Light‐Permeable Air Filter with Self‐Polarized Nylon‐11 Nanofibers for Enhanced Trapping of Particulate Matters

Exploring the Spatial Variation Characteristics and Influencing Factors of PM2.5 Pollution in China: Evidence from 289 Chinese Cities

PM2.5 concentration forecasting using Long Short-Term Memory Neural Network and Multi-Level Additive Model

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