scholarly journals Ground-Level PM2.5 Concentration Estimation from Satellite Data in the Beijing Area Using a Specific Particle Swarm Extinction Mass Conversion Algorithm

2018 ◽  
Vol 10 (12) ◽  
pp. 1906 ◽  
Author(s):  
Ying Li ◽  
Yong Xue ◽  
Jie Guang ◽  
Lu She ◽  
Cheng Fan ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Relative Humidity ◽  
Particle Swarm ◽  
Ground Level ◽  
Lookup Table ◽  
Beijing Area ◽  
Conversion Algorithm ◽  
Mass Conversion ◽  
Specific Particle ◽  
Pm2.5 Concentration

Particulate matter (PM) has a substantial influence on the environment, climate change and public health. Due to the limited spatial coverage of a ground-level PM2.5 monitoring system, the ground-based PM2.5 concentration measurement is insufficient in many circumstances. In this paper, a Specific Particle Swarm Extinction Mass Conversion Algorithm (SPSEMCA) using remotely sensed data is introduced. Ground-level observed PM2.5, planetary boundary layer height (PBLH) and relative humidity (RH) reanalyzed by the European Centre for Medium-Range Weather Forecasts (ECMWF) and aerosol optical depth (AOD), fine-mode fraction (FMF), particle size distribution, and refractive indices from AERONET (Aerosol Robotic Network) of the Beijing area in 2015 were used to establish this algorithm, and the same datasets for 2016 were used to test the performance of the SPSEMCA. The SPSEMCA involves four steps to obtain PM2.5 values from AOD datasets, and every step has certain advantages: (I) In the particle correction, we use η2.5 (the extinction fraction caused by particles with a diameter less than 2.5 μm) to make an accurate assimilation of AOD2.5, which is contributed to by the specific particle swarm PM2.5. (II) In the vertical correction, we compare the performance of PBLHc retrieved by satellite Lidar CALIPSO data and PBLHe reanalysis by ECMWF. Then, PBLHc is used to make a systematic correction for PBLHe. (III) For extinction to volume conversion, the relative humidity and the FMF are used together to assimilate the AVEC (averaged volume extinction coefficient, μm2/μm3). (IV) PM2.5 measured by ground-based air quality stations are used as the dry mass concentration when calculating the AMV (averaged mass volume, cm3/g) in humidity correction, that will avoid the uncertainties derived from the estimation of the particulate matter density ρ. (V) Multi-Angle Implementation of Atmospheric Correction (MAIAC) 1 km × 1 km AOD was used to retrieve high resolution PM2.5, and a LookUP Table-based Spectral Deconvolution Algorithm (LUT-SDA) FMF was used to avoid the large uncertainties caused by the MODIS FMF product. The validation of PM2.5 from the SPSEMCA algorithm to the AERONET observation data and MODIS monitoring data achieved acceptable results, R = 0.70, RMSE (root mean square error) = 58.75 μg/m3 for AERONET data, R = 0.75, RMSE = 43.38 μg/m3 for MODIS data, respectively. Furthermore, the trend of the temporal and spatial distribution of Beijing was revealed.

scholarly journals Boundary layer structure characteristics under objective classification of persistent pollution weather types in the Beijing area

2020 ◽  
Author(s):  
Zhaobin Sun ◽  
Xiujuan Zhao ◽  
Ziming Li ◽  
Guiqian Tang ◽  
Shiguang Miao
Keyword(s):  
Boundary Layer ◽  
Relative Humidity ◽  
Layer Structure ◽  
Synoptic Scale ◽  
Local Circulation ◽  
Weather Types ◽  
Beijing Area ◽  
Haze Pollution ◽  
Pollution Events ◽  
Pm2.5 Concentration

Abstract. Different types of pollution boundary layer structures form via the coupling of different synoptic systems and local mesoscale circulation in the boundary layer; this coupling contributes toward the formation and continuation of haze pollution. In this study, we objectively classify the 32 heavy haze pollution events using integrated meteorological and environmental data and ERA-Interim analysis data based on the rotated empirical orthogonal function method. The thermodynamic and dynamic structures of the boundary layer for different pollution weather types are synthesized, and the corresponding three-dimensional boundary layer conceptual models for haze pollution are constructed. The results show that four weather types mainly influence haze pollution events in the Beijing area: (a) type1: southerly transport, (b) type2: easterly convergence, (c) type3: sinking compression, and (d) type4: local accumulation. The explained variance in the four pollution weather types are 43.69 % (type1), 33.68 % (type2), 16.51 % (type3), and 3.92 % (type4). In persistent haze pollution events, type1 and type2 surpass 80 % on the first and second days, while the other types are present alternately in later stages. The atmospheric structures of type1, type2, and type3 have typical baroclinic characteristics at mid-high latitudes, indicating that the accumulation and transport of pollutants in the boundary layer is affected by coupled structures in synoptic-scale systems and local circulation. The atmospheric structure of type4 has typical barotropic characteristics, indicating that the accumulation and transport of pollutants is primarily affected by local circulation. In type1, southerly winds with a specific thickness and intensity prevail in the boundary layer, which is favorable for the accumulation of pollutants in plain areas along the Yan and Taihang Mountains, whereas haze pollution levels in other areas are relatively low. Due to the interaction between weak easterly winds and the western mountains, pollutants accumulate mainly in the plain areas along the Taihang Mountains in type2. The atmospheric vertical structure is not conducive to upward pollutant diffusion. In type3, the heights of the inversion and boundary layers are the lowest due to a weak sinking motion while relative humidity is the highest among the four types. The atmosphere has a small capacity for pollutant dispersion and is favorable to particulate matter hygroscopic growth; as a result, the type3 has highest PM2.5 concentration. In type4, the boundary layer is the highest among the four types, the relative humidity is the lowest, and the PM2.5 concentration is relatively lower under the influence of local mountain–plain winds. The findings of this study allow us to understand the inherent difference among heavy pollution boundary layers; in addition, they reveal the formation mechanism of haze pollution from an integrated synoptic scale and boundary layer structure perspective. We also provide scientific support for the scientific reduction of emissions and air quality prediction in the Beijing–Tianjin–Hebei region of China.

scholarly journals The Determinants of Mass Concentration of Indoor Particulate Matter in a Nursing Home

2010 ◽  
Vol 44-47 ◽  
pp. 3026-3030 ◽  
Author(s):  
Tsung Jung Cheng ◽  
Chih Yi Chang ◽  
Pei Ni Tsou ◽  
Ming Ju Wu ◽  
Yun Shu Feng
Keyword(s):  
Nursing Home ◽  
Particulate Matter ◽  
Relative Humidity ◽  
Mass Concentration ◽  
Explanatory Variables ◽  
Outdoor Environments ◽  
Pm2.5 And Pm10 ◽  
Indoor Particulate Matter ◽  
Indoor Pm2.5 ◽  
Pm2.5 Concentration

The study was conducted to evaluate the determinants of mass concentration of indoor particulate matter in a nursing home located in Taichung, Taiwan. PM2.5, PM10, temperature, relative humidity, CO, CO2, O3 and colony counts were collected in 2 bedrooms and their adjacent outdoor environments from November 2009 to January 2010. The results of multiple regression analysis suggested that the explanatory variables which included outdoor particle concentrations, indoor occupancy, different types of activities and ventilation accounted for 40.9% and 63.4% of the variance in the indoor PM2.5 concentration in Room A which is close to neighboring buildings and Room B which is close to main traffic, respectively. The explanatory variables accounted for 49.1% and 85.5% of the variance in the indoor PM10 concentration in Room A and B, respectively. Moreover, the result of correlation analysis showed that both indoor PM2.5 and PM10 concentrations were correlated to temperature, relative humidity and CO.

scholarly journals Spatial Analysis (Measurements at Heights of 10 m and 20 m above Ground Level) of the Concentrations of Particulate Matter (PM10, PM2.5, and PM1.0) and Gaseous Pollutants (H2S) on the University Campus: A Case Study

Atmosphere ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 62
Author(s):  
Robert Cichowicz ◽  
Maciej Dobrzański
Keyword(s):  
Spatial Distribution ◽  
Hydrogen Sulfide ◽  
Particulate Matter ◽  
Air Quality ◽  
Spatial Analysis ◽  
Ground Level ◽  
Leeward Side ◽  
High Concentration ◽  
Above Ground Level ◽  
High Concentrations

Spatial analysis of the distribution of particulate matter PM10, PM2.5, PM1.0, and hydrogen sulfide (H2S) gas pollution was performed in the area around a university library building. The reasons for the subject matter were reports related to the perceptible odor characteristic of hydrogen sulfide and a general poor assessment of air quality by employees and students. Due to the area of analysis, it was decided to perform measurements at two heights, 10 m and 20 m above ground level, using measuring equipment attached to a DJI Matrice 600 unmanned aerial vehicle (UAV). The aim of the measurements was air quality assessment and investigate the convergence of the theory of air flow around the building with the spatial distribution of air pollutants. Considerable differences of up to 63% were observed in the concentrations of pollutants measured around the building, especially between opposite sides, depending on the direction of the wind. To explain these differences, the theory of aerodynamics was applied to visualize the probable airflow in the direction of the wind. A strong convergence was observed between the aerodynamic model and the spatial distribution of pollutants. This was evidenced by the high concentrations of dust in the areas of strong turbulence at the edges of the building and on the leeward side. The accumulation of pollutants was also clearly noticeable in these locations. A high concentration of H2S was recorded around the library building on the side of the car park. On the other hand, the air turbulence around the building dispersed the gas pollution, causing the concentration of H2S to drop on the leeward side. It was confirmed that in some analyzed areas the permissible concentration of H2S was exceeded.

scholarly journals The Combined Effects of Fine Particulate Matter and Temperature on Preterm Birth in Seoul, 2010–2016

2021 ◽  
Vol 18 (4) ◽  
pp. 1463
Author(s):  
Youngrin Kwag ◽  
Min-ho Kim ◽  
Shinhee Ye ◽  
Jongmin Oh ◽  
Gyeyoon Yim ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Preterm Birth ◽  
Pregnant Women ◽  
Low Temperatures ◽  
Fine Particulate Matter ◽  
Temperature Data ◽  
Fine Particulate ◽  
The Mean ◽  
Pm2.5 Exposure ◽  
Pm2.5 Concentration

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.

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

Atmosphere ◽  
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.

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 ◽  
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.

scholarly journals Assessment of ambient dust pollution status at selected point sources (residential and commercial) of Mingaladon area, Yangon region, Myanmar

2018 ◽  
Vol 32 (1) ◽  
pp. 60-68 ◽  
Author(s):  
Sai Nyan Lin Tun ◽  
Than Htut Aung ◽  
Aye Sandar Mon ◽  
Pyay Hein Kyaw ◽  
Wattasit Siriwong ◽  
...  
Keyword(s):  
Wind Speed ◽  
Relative Humidity ◽  
Ambient Temperature ◽  
Traffic Congestion ◽  
Pm10 Concentration ◽  
Particulate Matters ◽  
Dust Pollution ◽  
Content Type ◽  
Pm2.5 And Pm10 ◽  
Pm2.5 Concentration

Purpose Dust (particulate matters) is very dangerous to our health as it is not visible with our naked eyes. Emissions of dust concentrations in the natural environment can occur mainly by road traffic, constructions and dust generating working environments. The purpose of this paper is to assess the ambient dust pollution status and to find out the association between PM concentrations and other determinant factors such as wind speed, ambient temperature, relative humidity and traffic congestion. Design/methodology/approach A cross-sectional study was conducted for two consecutive months (June and July, 2016) at a residential site (Defence Services Liver Hospital, Mingaladon) and a commercial site (Htouk-kyant Junction, Mingaladon) based on WHO Air Quality Reference Guideline Value (24-hour average). Hourly monitoring of PM2.5 and PM10 concentration and determinant factors such as traffic congestion, wind speed, ambient temperature and relative humidity for 24 hours a day was performed in both study sites. CW-HAT200 handheld particulate matters monitoring device was used to assess PM concentrations, temperature and humidity while traffic congestion was monitored by CCTV cameras. Findings The baseline PM2.5 and PM10 concentrations of Mingaladon area were (28.50±11.49)µg/m3 and (52.69±23.53)µg/m3, means 61.48 percent of PM2.5 concentration and 54.92 percent of PM10 concentration exceeded than the WHO reference value during the study period. PM concentration usually reached a peak during early morning (within 3:00 a.m.-5:00 a.m.) and at night (after 9:00 p.m.). PM2.5 concentration mainly depends on traffic congestion and temperature (adjusted R2=0.286), while PM10 concentration depends on traffic congestion and relative humidity (adjusted R2=0.292). Wind speed played a negative role in both PM2.5 and PM10 concentration with r=−0.228 and r=−0.266. Originality/value The air quality of the study area did not reach the satisfiable condition. The main cause of increased dust pollution in the whole study area was high traffic congestion (R2=0.63 and 0.60 for PM2.5 and PM10 concentration).

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