scholarly journals ESTIMATION OF PM<sub>2.5</sub> VERTICAL DISTRIBUTION USING CUSTOMIZED UAV AND MOBILE SENSORS IN BRGY. UP CAMPUS, DILIMAN, QUEZON CITY

2018 ◽  
Vol XLII-4/W9 ◽  
pp. 89-103 ◽  
Author(s):  
J. B. Babaan ◽  
J. P. Ballori ◽  
A. M. Tamondong ◽  
R. V. Ramos ◽  
P. M. Ostrea
Keyword(s):  
Air Quality ◽  
Wind Speed ◽  
Relative Humidity ◽  
Vertical Distribution ◽  
Unmanned Aerial Vehicle ◽  
Meteorological Parameters ◽  
Flying Height ◽  
Quezon City ◽  
Aerial Vehicle ◽  
The Vertical Distribution

<p><strong>Abstract.</strong> As the unmanned aerial vehicle (UAV) technology has gained popularity over the years, it has been introduced for air quality monitoring. This study demonstrates the feasibility of customized UAV with mobile monitoring devices as an effective, flexible, and alternative means to collect three-dimensional air pollutant concentration data. This also shows the vertical distribution of PM concentration and the relationship between the PM<sub>2.5</sub> vertical distribution and the meteorological parameters within 500<span class="thinspace"></span>m altitude on a single flight in UP Diliman, Quezon City. Measurement and mapping of the vertical distribution of particulate matter (PM)<sub>2.5</sub> concentration is demonstrated in this research using integrated air quality sensors and customized Unmanned Aerial Vehicle. The flight covers an area with a radius of 80 meters, following a cylindrical path with 40-meter interval vertically. The PM<sub>2.5</sub> concentration values are analyzed relative to the meteorological parameters including air speed, pressure, temperature, and relative humidity up to a 500<span class="thinspace"></span>meter-flying height in a single flight in Barangay UP Campus, UP Diliman, Quezon City. The study shows that generally, the PM<sub>2.5</sub> concentration decreases as the height increases with an exception in the 200&amp;ndash;280<span class="thinspace"></span>m above ground height interval due to a sudden change of atmospheric conditions at the time of the flight. Using correlation and regression analysis, the statistics shows that PM<sub>2.5</sub> concentration has a positive relationship with temperature and a negative relationship with relative humidity and wind speed. As relative humidity and wind speed increases, PM<sub>2.5</sub> decreases, while as temperature increases, PM<sub>2.5</sub> also increases.</p>

scholarly journals Spatiotemporal Variations of Particulate and Gaseous Pollutants and Their Relations to Meteorological Parameters: The Case of Xiangyang, China

2019 ◽  
Vol 17 (1) ◽  
pp. 136
Author(s):  
Wei Xue ◽  
Qingming Zhan ◽  
Qi Zhang ◽  
Zhonghua Wu
Keyword(s):  
Air Quality ◽  
Wind Speed ◽  
Relative Humidity ◽  
Air Pollutants ◽  
Meteorological Parameters ◽  
Air Pollutant ◽  
Weekend Effect ◽  
Gaseous Pollutants ◽  
Statistical Point ◽  
Particulate Pollutants

High air pollution levels have become a nationwide problem in China, but limited attention has been paid to prefecture-level cities. Furthermore, different time resolutions between air pollutant level data and meteorological parameters used in many previous studies can lead to biased results. Supported by synchronous measurements of air pollutants and meteorological parameters, including PM2.5, PM10, total suspended particles (TSP), CO, NO2, O3, SO2, temperature, relative humidity, wind speed and direction, at 16 urban sites in Xiangyang, China, from 1 March 2018 to 28 February 2019, this paper: (1) analyzes the overall air quality using an air quality index (AQI); (2) captures spatial dynamics of air pollutants with pollution point source data; (3) characterizes pollution variations at seasonal, day-of-week and diurnal timescales; (4) detects weekend effects and holiday (Chinese New Year and National Day holidays) effects from a statistical point of view; (5) establishes relationships between air pollutants and meteorological parameters. The principal results are as follows: (1) PM2.5 and PM10 act as primary pollutants all year round and O3 loses its primary pollutant position after November; (2) automobile manufacture contributes to more particulate pollutants while chemical plants produce more gaseous pollutants. TSP concentration is related to on-going construction and road sprinkler operations help alleviate it; (3) an unclear weekend effect for all air pollutants is confirmed; (4) celebration activities for the Chinese New Year bring distinctly increased concentrations of SO2 and thereby enhance secondary particulate pollutants; (5) relative humidity and wind speed, respectively, have strong negative correlations with coarse particles and fine particles. Temperature positively correlates with O3.

scholarly journals Influence of Meteorological Parameters on Air Quality at Hashemite University, Jordan

2017 ◽  
Vol 12 (2) ◽  
pp. 211-221
Author(s):  
Sana’a Odata ◽  
Abu- Allabanb ◽  
Khitam Odibatb
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Wind Speed ◽  
Relative Humidity ◽  
Air Temperature ◽  
Negative Correlation ◽  
Air Pollutants ◽  
Meteorological Parameters ◽  
Positive Correlation ◽  
Hashemite University

Four threshold air pollutants (SO2, NO, NO2, and O3) in addition to meteorological parameters were monitored at the Campus of the Hashemite University (HU) for two years (1/1/2012 through 30/12/013). Correlations between air pollution and meteorological parameters were derived. The results showed that O3 has a positive correlation with air temperature, wind speed and wind direction, but has a negative correlation with the relative humidity (RH). SO2 was found to have a negative correlation with the RH and wind speed, but positive correlation with air temperature. NO has negative correlation with air temperature, RH, and wind speed. And finally, NO2 has a negative correlation with RH and wind speed, but it has positive correlation with air temperature. Justify the reasons in brief with recommendations to improve the air quality

scholarly journals Mechanism of Spatiotemporal Air Quality Response to Meteorological Parameters: A National-Scale Analysis in China

2019 ◽  
Vol 11 (14) ◽  
pp. 3957 ◽  
Author(s):  
Zhi Qiao ◽  
Feng Wu ◽  
Xinliang Xu ◽  
Jin Yang ◽  
Luo Liu
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Wind Speed ◽  
Relative Humidity ◽  
Meteorological Parameters ◽  
Pollution Index ◽  
Hierarchical Cluster ◽  
Meteorological Conditions ◽  
Air Pollution Control ◽  
National Scale

The air quality over China exhibits seasonal and regional variation, resulting from heterogeneity in industrialization, and is highly affected by variability in meteorological conditions. We performed the first national-scale exploration of the relationship between the Air Pollution Index (API) and multiple meteorological parameters in China, using partial correlation and hierarchical cluster analyses. Relative humidity, wind speed, and temperature were the dominant factors influencing air quality year-round, due to their significant effects on pollutant dispersion and/or transformation of pollutants. The response of the API to single or multiple meteorological factors varied among cities and seasons, and a regional clustering of response mechanisms was observed, particularly in winter. Clear north–south differentiation was detected in the mechanisms of API response to relative humidity and wind speed. These findings provide insight into the spatiotemporal variation in air quality sensitivity to meteorological conditions, which will be useful for implementing regional air pollution control strategies.

The Concentration Characteristics of PM10 and PM2.5 in Eight Cities of China in March, 2013

2013 ◽  
Vol 807-809 ◽  
pp. 20-23 ◽  
Author(s):  
Tao Sheng ◽  
Jian Wu Shi ◽  
Sen Lin Tian ◽  
Li Mei Bi ◽  
Hao Deng ◽  
...  
Keyword(s):  
Air Quality ◽  
Wind Speed ◽  
Relative Humidity ◽  
Negative Correlation ◽  
Ambient Air ◽  
Quality Standard ◽  
Meteorological Factor ◽  
Urban Air ◽  
Ambient Air Quality ◽  
Ambient Air Quality Standard

According to the information of air quality which published by the urban air quality real-time publishing platform, the concentration characteristics of PM10 and PM2.5 were studied in Kunming (KM), Changsha (CS), Hangzhou (HZ), Shanghai (SH), Harbin (HEB), Beijing (BJ), Wuhan (WH) and Guangzhou (GZ). The results show that the concentrations of PM10 and PM2.5 exceeded the Ambient Air Quality Standard (GB3095-2012) in varying degrees in March, 2013. The concentrations of PM10 in Wuhan is the highest, reached 164μg/m3, exceeded the standard by 9.3%; the concentrations of PM2.5 is much higher in Wuhan, Changsha and Beijing, the average concentrations were 96μg/m3, 103μg/m3 and 110μg/m3, exceeded the standard by 28.0%, 37.3% and 46.7% respectively. The correlation of PM10 with PM2.5 in most of these cities was good in March. The correlation analysis of pollutant with meteorological factor in Hangzhou, Shanghai, Beijing and Guangzhou was also studied, the results show that the concentrations of PM10 and PM2.5 are weakly positive correlation with temperature in the four cities, negative correlation with relative humidity without Beijing, and negative correlation with wind speed.

scholarly journals The vertical distribution of biomass burning pollution over tropical South America from aircraft in situ measurements during SAMBBA

2019 ◽  
Vol 19 (9) ◽  
pp. 5771-5790 ◽  
Author(s):  
Eoghan Darbyshire ◽  
William T. Morgan ◽  
James D. Allan ◽  
Dantong Liu ◽  
Michael J. Flynn ◽  
...  
Keyword(s):  
Air Quality ◽  
South America ◽  
Vertical Distribution ◽  
Biomass Burning ◽  
Mixing Layer ◽  
Free Troposphere ◽  
The West ◽  
The Vertical Distribution ◽  
Tropical South America

Abstract. We examine processes driving the vertical distribution of biomass burning pollution following an integrated analysis of over 200 pollutant and meteorological profiles measured in situ during the South AMerican Biomass Burning Analysis (SAMBBA) field experiment. This study will aid future work examining the impact of biomass burning on weather, climate and air quality. During the dry season there were significant contrasts in the composition and vertical distribution of haze between western and eastern regions of tropical South America. Owing to an active or residual convective mixing layer, the aerosol abundance was similar from the surface to ∼1.5 km in the west and ∼3 km in the east. Black carbon mass loadings were double as much in the east (1.7 µg m−3) than the west (0.85 µg m−3), but aerosol scattering coefficients at 550 nm were similar (∼120 Mm−1), as too were CO near-surface concentrations (310–340 ppb). We attribute these contrasts to the more flaming combustion of Cerrado fires in the east and more smouldering combustion of deforestation and pasture fires in the west. Horizontal wind shear was important in inhibiting mixed layer growth and plume rise, in addition to advecting pollutants from the Cerrado regions into the remote tropical forest of central Amazonia. Thin layers above the mixing layer indicate the roles of both plume injection and shallow moist convection in delivering pollution to the lower free troposphere. However, detrainment of large smoke plumes into the upper free troposphere was very infrequently observed. Our results reiterate that thermodynamics control the pollutant vertical distribution and thus point to the need for correct model representation so that the spatial distribution and vertical structure of biomass burning smoke is captured. We observed an increase of aerosol abundance relative to CO with altitude both in the background haze and plume enhancement ratios. It is unlikely associated with thermodynamic partitioning, aerosol deposition or local non-fire sources. We speculate it may be linked to long-range transport from West Africa or fire combustion efficiency coupled to plume injection height. Further enquiry is required to explain the phenomenon and explore impacts on regional climate and air quality.

Autonomous Monitoring of Air Quality Through an Unmanned Aerial Vehicle

2019 ◽  
pp. 146-157
Author(s):  
Víctor H. Andaluz ◽  
Fernando A. Chicaiza ◽  
Geovanny Cuzco ◽  
Christian P. Carvajal ◽  
Jessica S. Ortiz ◽  
...  
Keyword(s):  
Air Quality ◽  
Unmanned Aerial Vehicle ◽  
Aerial Vehicle
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