A Deep Two-State Gated Recurrent Unit for Particulate Matter (PM2.5) Concentration Forecasting

Background: Preterm birth contributes to the morbidity and mortality of newborns and infants. Recent studies have shown that maternal exposure to particulate matter and extreme temperatures results in immune dysfunction, which can induce preterm birth. This study aimed to evaluate the association between fine particulate matter (PM2.5) exposure, temperature, and preterm birth in Seoul, Republic of Korea. Methods: We used 2010–2016 birth data from Seoul, obtained from the Korea National Statistical Office Microdata. PM2.5 concentration data from Seoul were generated through the Community Multiscale Air Quality (CMAQ) model. Seoul temperature data were collected from the Korea Meteorological Administration (KMA). The exposure period of PM2.5 and temperature were divided into the first (TR1), second (TR2), and third (TR3) trimesters of pregnancy. The mean PM2.5 concentration was used in units of ×10 µg/m3 and the mean temperature was divided into four categories based on quartiles. Logistic regression analyses were performed to evaluate the association between PM2.5 exposure and preterm birth, as well as the combined effects of PM2.5 exposure and temperature on preterm birth. Result: In a model that includes three trimesters of PM2.5 and temperature data as exposures, which assumes an interaction between PM2.5 and temperature in each trimester, the risk of preterm birth was positively associated with TR1 PM2.5 exposure among pregnant women exposed to relatively low mean temperatures (<3.4 °C) during TR1 (OR 1.134, 95% CI 1.061–1.213, p < 0.001). Conclusions: When we assumed the interaction between PM2.5 exposure and temperature exposure, PM2.5 exposure during TR1 increased the risk of preterm birth among pregnant women exposed to low temperatures during TR1. Pregnant women should be aware of the risk associated with combined exposure to particulate matter and low temperatures during TR1 to prevent preterm birth.

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Effectiveness of SOx, NOx, and Primary Particulate Matter Control Strategies in the Improvement of Ambient PM Concentration in Taiwan

Atmosphere ◽

10.3390/atmos12040460 ◽

2021 ◽

Vol 12 (4) ◽

pp. 460

Author(s):

Jiun-Horng Tsai ◽

Ming-Ye Lee ◽

Hung-Lung Chiang

Keyword(s):

Particulate Matter ◽

Air Quality ◽

Model Simulation ◽

Control Strategies ◽

Ambient Air ◽

Clean Air Act ◽

Control Measures ◽

Control Plan ◽

Clean Air ◽

Pm2.5 Concentration

The Community Multiscale Air Quality (CMAQ) measurement was employed for evaluating the effectiveness of fine particulate matter control strategies in Taiwan. There are three scenarios as follows: (I) the 2014 baseline year emission, (II) 2020 emissions reduced via the Clean Air Act (CAA), and (III) other emissions reduced stringently via the Clean Air Act. Based on the Taiwan Emission Data System (TEDs) 8.1, established in 2014, the emission of particulate matter 2.5 (PM2.5) was 73.5 thousand tons y−1, that of SOx was 121.3 thousand tons y−1, and that of NOx was 404.4 thousand tons y−1 in Taiwan. The CMAQ model simulation indicated that the PM2.5 concentration was 21.9 μg m−3. This could be underestimated by 24% in comparison with data from the ambient air quality monitoring stations of the Taiwan Environmental Protection Administration (TEPA). The results of the simulation of the PM2.5 concentration showed high PM2.5 concentrations in central and southwestern Taiwan, especially in Taichung and Kaohsiung. Compared to scenario I, the average annual concentrations of PM2.5 for scenario II and scenario III showed reductions of 20.1% and 28.8%, respectively. From the results derived from the simulation, it can be seen that control of NOx emissions may improve daily airborne PM2.5 concentrations in Taiwan significantly and control of directly emitted PM2.5 emissions may improve airborne PM2.5 concentrations each month. Nevertheless, the results reveal that the preliminary control plan could not achievethe air quality standard. Therefore, the efficacy and effectiveness of the control measures must be considered to better reduce emissions in the future.

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Characterization of the emission behavior of pulse-jet cleaned filters using a low-cost particulate matter sensor/Charakterisierung der Emission von druckstoßgereinigten Oberflächenfiltern mit einem Low-Cost-Feinstaubsensor

Gefahrstoffe ◽

10.37544/0949-8036-2019-11-12-49 ◽

2019 ◽

Vol 79 (11-12) ◽

pp. 443-450

Author(s):

P. Bächler ◽

J. Meyer ◽

A. Dittler

Keyword(s):

Particle Size ◽

Particulate Matter ◽

Particle Size Distribution ◽

Size Distribution ◽

Low Cost ◽

Particle Emission ◽

Filter Medium ◽

Emission Measurement ◽

Pm2.5 Concentration ◽

Pulse Jet

The reduction of fine dust emissions with pulse-jet cleaned filters plays an important role in industrial gas cleaning to meet emission standards and protect the environment. The dust emission of technical facilities is typically measured “end of pipe”, so that no information about the local emission contribution of individual filter elements exists. Cheap and compact low-cost sensors for the detection of particulate matter (PM) concentrations, which have been prominently applied for immission monitoring in recent years have the potential for emission measurement of filters to improve process monitoring. This publication discusses the suitability of a low-cost PM-sensor, the model SPS30 from the manufacturer Sensirion, in terms of the potential for particle emission measurement of surface filters in a filter test rig based on DIN ISO 11057. A Promo® 2000 in combination with a Welas® 2100 sensor serves as the optical reference device for the evaluation of the detected PM2.5 concentration and particle size distribution of the emission measured by the low-cost sensor. The Sensirion sensor shows qualitatively similar results of the detected PM2.5 emission as the low-cost sensor SDS011 from the manufacturer Nova Fitness, which was investigated by Schwarz et al. in a former study. The typical emission peak after jet-pulse cleaning of the filter, due to the penetration of particles through the filter medium, is detected during Δp-controlled operation. The particle size distribution calculated from the size resolved number concentrations of the low-cost sensor yields a distinct distribution for three different employed filter media and qualitatively fits the size distribution detected by the Palas® reference. The emission of these three different types of filter media can be distinguished clearly by the measured PM2.5 concentration and the emitted mass per cycle and filter area, demonstrating the potential for PM emission monitoring by the low-cost PM-sensor. During the period of Δt-controlled filter aging, a decreasing emission, caused by an increasing amount of stored particles in the filter medium, is detected. Due to the reduced particle emission after filter aging, the specified maximum concentration of the low-cost sensor is not exceeded so that coincidence is unlikely to affect the measurement results of the sensor for all but the very first stage of filter life.

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A social network analysis of tourism activity patterns among domestic tourists influenced by fine particulate matter (PM2.5) concentration

Korean Journal of Hospitality & Tourism ◽

10.24992/kjht.2021.10.30.07.237 ◽

2021 ◽

Vol 30 (7) ◽

pp. 237-252

Author(s):

Deuk-Hee Park ◽

Chul-Hee Lim

Keyword(s):

Particulate Matter ◽

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

Activity Patterns ◽

Fine Particulate Matter ◽

Fine Particulate ◽

Tourism Activity ◽

Pm2.5 Concentration

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Effect of modes of transportation on commuters' exposure to fine particulate matter (PM2.5) and nitrogen dioxide (NO2) in Chennai, India

Environmental Engineering Research ◽

10.4491/eer.2019.380 ◽

2019 ◽

Vol 25 (6) ◽

pp. 898-907 ◽

Cited By ~ 2

Author(s):

M. Gokul Raj ◽

S. Karthikeyan

Keyword(s):

Particulate Matter ◽

Nitrogen Dioxide ◽

Urban Community ◽

Fine Particulate Matter ◽

Exposure Concentration ◽

Ambient Particulate Matter ◽

Fine Particulate ◽

The Mean ◽

Maximum Exposure ◽

Pm2.5 Concentration

Daily commuting increases level of contaminants inhaled by urban community and it is influenced by mode and time of commuting. In this study, the commuters’ exposure to ambient particulate matter (PM2.5) and nitrogen dioxide (NO2) was assessed during three modes of travel in six different road stretches of Chennai. The mean distance of road stretches was 25 km and the exposure to pollutants was assessed during peak hours and off-peak hours. The average travel duration was in the range of 39 to 91 min in motorbike, 83 to 140 min in car and 110 to 161 min in bus. Though there was variation on exposure to concentration in modes of transportation, the maximum exposure concentration of PM2.5 was observed as 709 μg/m3 in bus and the minimum exposure concentration was 29 μg/m3 in closed car. Similarly, the maximum exposure concentration of NO2 was observed to be 312 μg/m3 in bus and the minimum exposure concentration was 21 μg/m3 in car. The concentration of elements in PM2.5 was in the order of Si > Na > Ca > Al ≥ K > S ≥ Cd, with Si and Cd concentration as 60% and < 1% of the PM2.5 concentration.

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Visualization of Correlation for Air Pollution Time Series using TAQMN Data

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.c6620.098319 ◽

2019 ◽

Vol 8 (3) ◽

pp. 7922-7927

Keyword(s):

Air Pollution ◽

Time Series ◽

Particulate Matter ◽

Pearson Correlation ◽

Fine Particulate Matter ◽

Correlation Method ◽

Well Being ◽

Pollution Levels ◽

Fine Particulate ◽

Pm2.5 Concentration

In Taiwan country Annan, Chiayi, Giran, and Puzi cities are facing a serious fine particulate matter (PM2.5) issue. To date the impressive advance has been made toward understanding the PM2.5 issue, counting special temporal characterization, driving variables and well-being impacted. However, notable research as has been done on the interaction of the content between the selected cities of Taiwan country for particulate matter (PM2.5) concentration. In this paper, we purposed a visualization technique based on this principle of the visualization, cross-correlation method and also the time-series concentration with particulate matter (PM2.5) for different cities in Taiwan. The visualization also shows that the correlation between the different meteorological factors as well as the different air pollution pollutants for particular cities in Taiwan. This visualization approach helps to determine the concentration of the air pollution levels in different cities and also determine the Pearson correlation, r values of selected cities are Annan, Puzi, Giran, and Wugu.

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Short-Term Associations of Fine Particulate Matter and Synoptic Weather Types with Cardiovascular Mortality: An Ecological Time-Series Study in Shanghai, China

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17031111 ◽

2020 ◽

Vol 17 (3) ◽

pp. 1111 ◽

Cited By ~ 1

Author(s):

Qing Tian ◽

Mei Li ◽

Scott Montgomery ◽

Bo Fang ◽

Chunfang Wang ◽

...

Keyword(s):

Particulate Matter ◽

Cardiovascular Mortality ◽

Fine Particulate Matter ◽

Weather Conditions ◽

Cold Weather ◽

Short Term ◽

Weather Types ◽

Synoptic Weather ◽

Fine Particulate ◽

Pm2.5 Concentration

Background: Exposures to both ambient fine particulate matter (PM2.5) and extreme weather conditions have been associated with cardiovascular disease (CVD) deaths in numerous epidemiologic studies. However, evidence on the associations with CVD deaths for interaction effects between PM2.5 and weather conditions is still limited. This study aimed to investigate associations of exposures to PM2.5 and weather conditions with cardiovascular mortality, and further to investigate the synergistic or antagonistic effects of ambient air pollutants and synoptic weather types (SWTs). Methods: Information on daily CVD deaths, air pollution, and meteorological conditions between 1 January 2012 and 31 December 2014 was obtained in Shanghai, China. Generalized additive models were used to assess the associations of daily PM2.5 concentrations and meteorological factors with CVD deaths. A 15-day lag analysis was conducted using a polynomial distributed lag model to access the lag patterns for associations with PM2.5. Results: During the study period, the total number of CVD deaths in Shanghai was 59,486, with a daily mean of 54.3 deaths. The average daily PM2.5 concentration was 55.0 µg/m3. Each 10 µg/m3 increase in PM2.5 concentration was associated with a 1.26% (95% confidence interval (CI): 0.40%, 2.12%) increase in CVD mortality. No SWT was statistically significantly associated with CVD deaths. For the interaction between PM2.5 and SWT, statistically significant interactions were found between PM2.5 and cold weather, with risk for PM2.5 in cold dry SWT decreasing by 1.47% (95% CI: 0.54%, 2.39%), and in cold humid SWT the risk decreased by 1.45% (95% CI: 0.52%, 2.36%). In the lag effect analysis, statistically significant positive associations were found for PM2.5 in the 1–3 lag days, while no statistically significant effects were found for other lag day periods. Conclusions: Exposure to PM2.5 was associated with short-term increased risk of cardiovascular deaths with some lag effects, while the cold weather may have an antagonistic effect with PM2.5. However, the ecological study design limited the possibility to identify a causal relationship, so prospective studies with individual level data are warranted.

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Variation Trends of Fine Particulate Matter Concentration in Wuhan City from 2013 to 2017

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph15071487 ◽

2018 ◽

Vol 15 (7) ◽

pp. 1487 ◽

Cited By ~ 5

Author(s):

Daoru Liu ◽

Qinli Deng ◽

Zeng Zhou ◽

Yaolin Lin ◽

Junwei Tao

Keyword(s):

Particulate Matter ◽

Fine Particulate Matter ◽

Wuhan City ◽

Fine Particulate ◽

Variation Patterns ◽

Average Proportion ◽

June July ◽

Matter Concentration ◽

Mean Concentration ◽

Pm2.5 Concentration

Fine particulate matter (PM2.5) is directly associated with smog and has become the primary factor that threatens air quality in China. In order to investigate the variation patterns of PM2.5 concentrations in various regions of Wuhan city across different time spans, we analyzed continuous monitoring data from six monitoring sites in Wuhan city from 2013 to 2017. The results showed that the PM2.5 concentration from the various monitoring sites in the five-year period showed a decreasing trend. January, October, and December are the three months with relatively high mean monthly PM2.5 concentrations in the year, while June, July, and August are the three months with relatively low mean monthly PM2.5 concentrations in the year. The number of days with a daily mean concentration of 35–75 μg/m3 was the highest, while the number of days with a daily mean concentration of more than 250 μg/m3 was the lowest. PM2.5 accounted for a large proportion of the major pollutants and is the main source of air pollution in Wuhan city, with an average proportion of over 46%.

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Urban PM2.5 Concentration Prediction via Attention-Based CNN–LSTM

Applied Sciences ◽

10.3390/app10061953 ◽

2020 ◽

Vol 10 (6) ◽

pp. 1953 ◽

Cited By ~ 3

Author(s):

Songzhou Li ◽

Gang Xie ◽

Jinchang Ren ◽

Lei Guo ◽

Yunyun Yang ◽

...

Keyword(s):

Particulate Matter ◽

Air Quality ◽

Air Pollutant ◽

Quality Data ◽

Coefficient Of Determination ◽

Series Data ◽

The Past ◽

Lstm Network ◽

Concentration Prediction ◽

Pm2.5 Concentration

Urban particulate matter forecasting is regarded as an essential issue for early warning and control management of air pollution, especially fine particulate matter (PM2.5). However, existing methods for PM2.5 concentration prediction neglect the effects of featured states at different times in the past on future PM2.5 concentration, and most fail to effectively simulate the temporal and spatial dependencies of PM2.5 concentration at the same time. With this consideration, we propose a deep learning-based method, AC-LSTM, which comprises a one-dimensional convolutional neural network (CNN), long short-term memory (LSTM) network, and attention-based network, for urban PM2.5 concentration prediction. Instead of only using air pollutant concentrations, we also add meteorological data and the PM2.5 concentrations of adjacent air quality monitoring stations as the input to our AC-LSTM. Hence, the spatiotemporal correlation and interdependence of multivariate air quality-related time-series data are learned by the CNN–LSTM network in AC-LSTM. The attention mechanism is applied to capture the importance degrees of the effects of featured states at different times in the past on future PM2.5 concentration. The attention-based layer can automatically weigh the past feature states to improve prediction accuracy. In addition, we predict the PM2.5 concentrations over the next 24 h by using air quality data in Taiyuan city, China, and compare it with six baseline methods. To compare the overall performance of each method, the mean absolute error (MAE), root-mean-square error (RMSE), and coefficient of determination (R2) are applied to the experiments in this paper. The experimental results indicate that our method is capable of dealing with PM2.5 concentration prediction with the highest performance.

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Exposure Assessment of Environmental Tobacco Aerosol from Heated Tobacco Products: Nicotine and PM Exposures under Two Limited Conditions

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17228536 ◽

2020 ◽

Vol 17 (22) ◽

pp. 8536

Author(s):

Tomoyasu Hirano ◽

Tokuaki Shobayashi ◽

Teiji Takei ◽

Fumihiko Wakao

Keyword(s):

Particulate Matter ◽

Indoor Air ◽

Indoor Environment ◽

Tobacco Products ◽

Health Concerns ◽

Standard Value ◽

Preliminary Study ◽

The Impact ◽

Indoor Pm2.5 ◽

Pm2.5 Concentration

It is too early to provide a clear answer on the impact of exposure to the second-hand aerosol of heated tobacco products (HTPs) in the planning of policy for smoke-free indoors legislation. Here, we conducted a preliminary study to evaluate indoor air quality with the use of HTPs. We first measured the concentration of nicotine and particulate matter (PM2.5) in the air following 50 puffs in the use of HTPs or cigarettes in a small shower cubicle. We then measured these concentrations in comparison with the use equivalent of smoking 5.4 cigarettes per hour in a 25 m3 room, as a typical indoor environment test condition. In the shower cubicle test, nicotine concentrations in indoor air using three types of HTP, namely IQOS, glo, and ploomTECH, were 25.9–257 μg/m3. These values all exceed the upper bound of the range of tolerable concentration without health concerns, namely 3 µg/m3. In particular, the indoor PM2.5 concentration of about 300 to 500 μg/m3 using IQOS or glo in the shower cubicle is hazardous. In the 25 m3 room test, in contrast, nicotine concentrations in indoor air with the three types of HTP did not exceed 3 μg/m3. PM2.5 concentrations were below the standard value of 15 μg/m3 per year for IQOS and ploomTECH, but were slightly high for glo, with some measurements exceeding 100 μg/m3. These results do not negate the inclusion of HTPs within a regulatory framework for indoor tolerable use from exposure to HTP aerosol, unlike cigarette smoke.

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