scholarly journals Influence of meteorological factors on variations of particulate matter (PM10) concentration during haze episodes in Malaysia

2018 ◽  
Author(s):  
N. I. M. Hashim ◽  
N. M. Noor ◽  
S. Annas
Keyword(s):  
Particulate Matter ◽  
Meteorological Factors ◽  
Pm10 Concentration

scholarly journals Particulate Matter 10 (PM10) Is Associated with Epistaxis in Children and Adults

2021 ◽  
Vol 18 (9) ◽  
pp. 4809
Author(s):  
Kyungsoo Kim ◽  
Il-Youp Kwak ◽  
Hyunjin Min
Keyword(s):  
Particulate Matter ◽  
Environmental Factor ◽  
Meteorological Factors ◽  
Pm10 Concentration ◽  
Cumulative Number ◽  
Atmospheric Concentration ◽  
Concentration Data ◽  
Research Attention ◽  
Korea Meteorological Administration ◽  
The Impact

The impact of atmospheric concentration of particulate matter ≤10 μm in diameter (PM10) continues to attract research attention. This study aimed to evaluate the effects of meteorological factors, including PM10 concentration, on epistaxis presentation in children and adults. We reviewed the data from 1557 days and 2273 cases of epistaxis between January 2015 and December 2019. Eligible patients were stratified by age into the children (age ≤17 years) and adult groups. The main outcome was the incidence and cumulative number of epistaxis presentations in hospital per day and month. Meteorological factors and PM10 concentration data were obtained from the Korea Meteorological Administration. Several meteorological factors were associated with epistaxis presentation in hospital; however, these associations differed between children and adults. Only PM10 concentration was consistently associated with daily epistaxis presentation in hospital among both children and adults. Additionally, PM10 concentration was associated with the daily cumulative number of epistaxis presentations in hospital in children and adults. Furthermore, the monthly mean PM10 concentration was significantly associated with the total number of epistaxis presentations in the corresponding month. PM10 concentration should be regarded as an important environmental factor that may affect epistaxis in both children and adults.

scholarly journals Relationships between Meteorological Parameters and Particulate Matter in Mae Hong Son Province, Thailand

2018 ◽  
Vol 15 (12) ◽  
pp. 2801 ◽  
Author(s):  
Wissanupong Kliengchuay ◽  
Aronrag Cooper Meeyai ◽  
Suwalee Worakhunpiset ◽  
Kraichat Tantrakarnapa
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Meteorological Parameters ◽  
Warning System ◽  
Correlation Coefficients ◽  
Pm10 Concentration ◽  
P Value ◽  
Climate Data ◽  
High Concentration ◽  
Ambient Concentrations

Meteorological parameters play an important role in determining the prevalence of ambient particulate matter (PM) in the upper north of Thailand. Mae Hong Son is a province located in this region and which borders Myanmar. This study aimed to determine the relationships between meteorological parameters and ambient concentrations of particulate matter less than 10 µm in diameter (PM10) in Mae Hong Son. Parameters were measured at an air quality monitoring station, and consisted of PM10, carbon monoxide (CO), ozone (O3), and meteorological factors, including temperature, rainfall, pressure, wind speed, wind direction, and relative humidity (RH). Nine years (2009–2017) of pollution and climate data obtained from the Thai Pollution Control Department (PCD) were used for analysis. The results of this study indicate that PM10 is influenced by meteorological parameters; high concentration occurred during the dry season and northeastern monsoon seasons. Maximum concentrations were always observed in March. The PM10 concentrations were significantly related to CO and O3 concentrations and to RH, giving correlation coefficients of 0.73, 0.39, and −0.37, respectively (p-value < 0.001). Additionally, the hourly PM10 concentration fluctuated within each day. In general, it was found that the reporting of daily concentrations might be best suited to public announcements and presentations. Hourly concentrations are recommended for public declarations that might be useful for warning citizens and organizations about air pollution. Our findings could be used to improve the understanding of PM10 concentration patterns in Mae Hong Son and provide information to better air pollution measures and establish a warning system for the province.

scholarly journals Spatiotemporal trend of particulate matter (PM10) concentration on cement industries in Klapanunggal and Citeureup Sub-districts

2020 ◽  
Vol 561 ◽  
pp. 012035
Author(s):  
R A Pambudi ◽  
A Naldi ◽  
A Luthfi ◽  
D A Puspitarini ◽  
M M Chaerani ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Pm10 Concentration

scholarly journals How Did Distribution Patterns of Particulate Matter Air Pollution (PM2.5 and PM10) Change in China during the COVID-19 Outbreak: A Spatiotemporal Investigation at Chinese City-Level

2020 ◽  
Vol 17 (17) ◽  
pp. 6274 ◽  
Author(s):  
Zhiyu Fan ◽  
Qingming Zhan ◽  
Chen Yang ◽  
Huimin Liu ◽  
Meng Zhan
Keyword(s):  
Air Pollution ◽  
Regression Analysis ◽  
Particulate Matter ◽  
Socioeconomic Factors ◽  
Influencing Factors ◽  
Geographically Weighted Regression ◽  
Meteorological Factors ◽  
Central China ◽  
Weighted Regression ◽  
Pm2.5 And Pm10

Due to the suspension of traffic mobility and industrial activities during the COVID-19, particulate matter (PM) pollution has decreased in China. However, rarely have research studies discussed the spatiotemporal pattern of this change and related influencing factors at city-scale across the nation. In this research, the clustering patterns of the decline rates of PM2.5 and PM10 during the period from 20 January to 8 April in 2020, compared with the same period of 2019, were investigated using spatial autocorrelation analysis. Four meteorological factors and two socioeconomic factors, i.e., the decline of intra-city mobility intensity (dIMI) representing the effect of traffic mobility and the decline rates of the secondary industrial output values (drSIOV), were adopted in the regression analysis. Then, multi-scale geographically weighted regression (MGWR), a model allowing the particular processing scale for each independent variable, was applied for investigating the relationship between PM pollution reductions and influencing factors. For comparison, ordinary least square (OLS) regression and the classic geographically weighted regression (GWR) were also performed. The research found that there were 16% and 20% reduction of PM2.5 and PM10 concentration across China and significant PM pollution mitigation in central, east, and south regions of China. As for the regression analysis results, MGWR outperformed the other two models, with R2 of 0.711 and 0.732 for PM2.5 and PM10, respectively. The results of MGWR revealed that the two socioeconomic factors had more significant impacts than meteorological factors. It showed that the reduction of traffic mobility caused more relative declines of PM2.5 in east China (e.g., cities in Jiangsu), while it caused more relative declines of PM10 in central China (e.g., cities in Henan). The reduction of industrial operation had a strong relationship with the PM10 drop in northeast China. The results are crucial for understanding how the decline pattern of PM pollution varied spatially during the COVID-19 outbreak, and it also provides a good reference for air pollution control in the future.

Concentration Features and Elemental Characteristics of PM10 in Brown Haze Episode

2010 ◽  
Vol 113-116 ◽  
pp. 1661-1664
Author(s):  
Li Kun Huang ◽  
Chung Shin Yuan ◽  
Guang Zhi Wang ◽  
Kun Wang
Keyword(s):  
Particulate Matter ◽  
Wind Speed ◽  
Relative Humidity ◽  
Human Activities ◽  
Meteorological Factors ◽  
Dew Point ◽  
Metallic Elements ◽  
Haze Episode ◽  
Respirable Particulate ◽  
Haze Days

The correlation between PM10 and meteorological factors were investigated, such as wind speed, atmospheric visibility, dew point, relative humidity, and ambient temperature during the brown haze episode. In order to identify the elemental characteristics and concentration features of PM10 during brown haze episode, respirable particulate matter (PM10) was collected during non-haze days and haze days and further analyzed for 20 elements. Among the metallic elements, S, K, Si, and Ca contributed major composition of PM10. S came mainly from coal burning and K was mainly attributed from incinerators and abandoned biomass burning. Furthermore, As was not detectable in non-haze days, while its concentration was 0.15~0.17 μg/m3 in haze days, which would be very much harmful to human health. However, the variation of Sr, Ti, Cr, and Cd was insignificantly, mainly due to low relevance with human activities and/or cross-boundary transportation.

scholarly journals The evaluation on artificial neural networks (ANN) and multiple linear regressions (MLR) models over particulate matter (PM10) variability during haze and non-haze episodes: A decade case study

2019 ◽  
Vol 15 (2) ◽  
pp. 164-172 ◽  
Author(s):  
Ku Mohd Kalkausar Ku Yusof ◽  
Azman Azid ◽  
Muhamad Shirwan Abdullah Sani ◽  
Mohd Saiful Samsudin ◽  
Siti Noor Syuhada Muhammad Amin ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Particulate Matter ◽  
Meteorological Factors ◽  
Model Development ◽  
Eastern Region ◽  
Multiple Linear Regressions ◽  
Artificial Neural ◽  
Linear Regressions

The comprehensives of particulate matter studies are needed in predicting future haze occurrences in Malaysia. This paper presents the application of Artificial Neural Networks (ANN) and Multiple Linear Regressions (MLR) coupled with sensitivity analysis (SA) in order to recognize the pollutant relationship status over particulate matter (PM10) in eastern region. Eight monitoring studies were used, involving 14 input parameters as independent variables including meteorological factors. In order to investigate the efficiency of ANN and MLR performance, two different weather circumstances were selected; haze and non-haze. The performance evaluation was characterized into two steps. Firstly, two models were developed based on ANN and MLR which denoted as full model, with all parameters (14 variables) were used as the input. SA was used as additional feature to rank the most contributed parameter to PM10 variations in both situations. Next, the model development was evaluated based on selected model, where only significant variables were selected as input. Three mathematical indices were introduced (R2, RMSE and SSE) to compare on both techniques. From the findings, ANN performed better in full and selected model, with both models were completely showed a significant result during hazy and non-hazy. On top of that, UVb and carbon monoxide were both variables that mutually predicted by ANN and MLR during hazy and non-hazy days, respectively. The precise predictions were required in helping any related agency to emphasize on pollutant that essentially contributed to PM10 variations, especially during haze period.

Dew condensation during a typical haze event in Changchun, China

2019 ◽  
Vol 11 (2) ◽  
pp. 568-576
Author(s):  
Yingying Xu ◽  
Zhaoqing Luan ◽  
Hui Zhu
Keyword(s):  
Particulate Matter ◽  
Water Vapor ◽  
Meteorological Factors ◽  
Environmental Problems ◽  
Underlying Surface ◽  
Removal Rates ◽  
Near Surface ◽  
Haze Event ◽  
Dew Amount ◽  
Mass Concentrations

Abstract Haze is one of the most serious environmental problems affecting China. This study monitored the changes in dew amount and quality during a haze event that occurred in 2016. Water vapor migrated continuously to the near surface during the haze event. The period of dew condensation increased because of meteorological factors, and the daily dew amount (0.178 mm) was higher during the haze event than in non-haze weather (0.0607 mm). The concentrations of all ions in the dew increased gradually during the haze event, peaking during the most serious period of the haze. The concentrations of SO42− and NH4+ reached 15,325.95 and 13,865.45 μeq/L, which were 2.24 and 10.83 times greater than those obtained before the haze event, respectively. During the haze event, the particulate matter (PM) concentrations within the dew increased, and the mass concentrations of PM2.5 and PM2.5-10 during the worst haze event were 65.3 and 166.1 mg/L, respectively. The dew mainly removed coarse PM; the average removal rates of PM2.5 and PM2.5-10 during the haze event were 13.6% and 16.9%, respectively. Dew can capture PM throughout a haze event, and its purifying effect on the underlying surface was obvious, especially during the beginning of the event.

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