Jeevan Regmi1,2, Khem N. Poudyal3, Amod Pokhrel4, Nabin Malakar5, Madhu Gyawali6, Lekhendra Tripathee7, Mukesh Rai7, Srikanthan Ramachandran8, Katrina Wilson9, Rudra Aryal This email address is being protected from spambots. You need JavaScript enabled to view it.9

1 Prithvi Narayan Campus, Tribhuvan University (TU), Pokhara, Nepal
2 Central Department of Physics, Tribhuvan University, Kirtipur, Nepal
3 Department of Applied Sciences, IOE Pulchowk Campus, Tribhuvan University, Lalitpur, Nepal
4 University of California Berkeley, California, USA
5 Worcester State University, Massachusetts, USA
6 Department of Physics, San Jacinto College, South Campus, Houston, TX 77089, USA
7 State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
8 Physical Research Laboratory, Ahmedabad, India
9 Franklin Pierce University, Rindge, NH, USA


Received: September 20, 2022
Revised: November 28, 2022
Accepted: December 15, 2022

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.4209/aaqr.220311  


Cite this article:

Regmi, J., Poudyal, K.N., Pokhrel, A., Malakar, N., Gyawali, M., Tripathee, L., Rai, M., Ramachandran, S., Wilson, K., Aryal, R. (2023). Analysis of Surface Level PM2.5 Measured by Low-Cost Sensor and Satellite-Based Column Aerosol Optical Depth (AOD) over Kathmandu. Aerosol Air Qual. Res. 23, 220311. https://doi.org/10.4209/aaqr.220311


HIGHLIGHTS

  • Surface level PM2.5 concentrations are bimodal, peaking in the morning and evening.
  • RH correction factor improves the correlation between surface PM2.5 and column AOD.
  • Transboundary Air pollution contributes to the atmospheric column of Kathmandu.
 

ABSTRACT


A low-cost PurpleAir PA-II sensor was installed, in 2020 at the Institute of Engineering (IOE) Pulchowk Campus, TU located in Kathmandu valley, Nepal, to measure particulate matter with an aerodynamic diameter equal to or smaller than 2.5 µm (PM2.5). The observation shows that hourly averaged PM2.5 fluctuates bimodally in four seasons (Winter: December, January, and February; Spring: March–May; Summer: June–September; and Autumn: October–November), with the highest levels occurring during morning and evening rush hours. PurpleAir records PM2.5 with a maximum average of 101 ± 26.31 µg m3, in winter, 55.58 ± 11.42 µg m3, in spring, 45.46 ± 12.16 µg m3, in autumn, and a minimum of 22.78 ± 3.23 µg m3, in the summer. Due to rain and diffusion in the vertical atmosphere, PM2.5 levels are lowest during the summer. The ± number for each season represents the standard deviation from the hourly average. AOD550nm data collected by MODIS (Moderate Resolution Imaging Spectroradiometer) onboard two NASA satellites, Terra and Aqua, are compared with simultaneously observed PM2.5. With humidity correction factor f(RH), R2 increases from 0.413 to 0.608 (in winter), 0.426 to 0.508 (in spring), and 0.083 to 0.293 (in autumn). The summer AOD data and PM2.5 are not compared due to a lack of AOD observations. By comparing the column-integrated aerosol data with the surface-level aerosol concentration, this study illustrates the relevance of atmospheric parameters while investigating the reliability of PurpleAir measurements. A cluster analysis of five-day back trajectories of air masses arriving at different altitudes in different seasons indicates that long-range transport of air pollution contributes to MODIS's column integrated AOD by adding aerosol population.


Keywords: Aerosol optical depth, MODIS, PM2.5, PurpleAir, Transboundary aerosols


1 INTRODUCTION


The study of atmospheric aerosols, particulate matter, is important since they influence Earth's radiation budget by scattering and absorbing incoming solar radiation and altering cloud microphysical properties (Haywood and Boucher, 2000; Ramanathan et al., 2001). Their ability to affect Earth's radiation budget and their effects on health, air quality, and clouds are strongly elated to their size (Dusek et al., 2006; Ruzer and Harley, 2005). A PM2.5 particle has an aerodynamic diameter of less than 2.5 micrometers. Particulates of this size can be either natural or anthropogenic, and their composition varies according to their sources (Devi et al., 2020). Human-produced particles are generally smaller in size, while naturally produced particles are larger. PM2.5 is also one of the most common pollutants contributing to global health burdens (Lim et al., 2012). According to Shiraiwa et al. (2017), these particles penetrate deeply into the lungs of humans and cause adverse health effects depending on their chemical composition. Statistical analysis shows a strong correlation between hospitalizations, and deaths and exposure to high PM2.5 concentrations (Di et al., 2017; Schwartz et al., 1996; Klemm and Mason, 2000). As a result, a comprehensive measurement and analysis of particle concentration in the atmosphere are essential for protecting local health and studying atmospheric conditions.

Several studies highlight the importance of low-cost air quality monitoring instruments, such as PurpleAir Monitors, for determining both spatial and temporal air quality, since traditional real-time air quality monitors lack this capability due to their high installation and maintenance costs (Kumar et al., 2018; Munir et al., 2019). Because of their availability, size, and price, PurpleAir monitors are being widely used in almost every continent to measure PM concentrations on surfaces, as well as for assessing air pollution (Ouimette et al., 2022).

These monitors work on the light scattering principle in which a fan draws air particles into the chamber and passes along the laser path. The sensor's photodiode detector converts scattered light into voltage pulses from light. These pulses use to count the number of particles of sizes 0.3, 0.5, 1, 2.5, 5, and 10 µm and determine mass concentrations of PM1.0, PM2.5, and PM10 by using an algorithm for PM concentration (Ardon-Dryer et al., 2020; Sayahi et al., 2019); https://www2.​purpleair.com/community/faq). In a variety of environmental conditions, PurpleAir monitors were evaluated for measuring atmospheric particle concentrations (Morawska et al., 2018; Stavroulas et al., 2020; Zhang et al., 2009). Ardon-Dryer et al. (2020) showed that PurpleAir PM2.5 data significantly correlated with PM2.5 measured by AQMS (Air Quality Monitoring Stations) operated by the Environmental Protection Agency. This study will also aid in investigating the low-cost monitors for measuring air pollution by comparing them with other reliable reference data, such as satellite-based aerosol optical properties. The simultaneous measurements of aerosols at ground level and those based on remote sensing provide reliable information about air pollutant loading over the observation site (Baral and Thapa, 2021; Segura et al., 2017). Comparison of surface and atmospheric level aerosol data can also be used as a statistical basis to extrapolate regional aerosol data (Aryal et al., 2014; Moody et al., 2014).

In atmospheric column observations, satellites provide long-term data and global coverage, providing insights into regional air quality and climate variations through the precise timing and spatial resolution of PM2.5 concentrations (Karagulian et al., 2015; Schwarze et al., 2006; Zhong et al., 2015). Satellites, however, view the entire atmosphere column, which makes it difficult to distinguish surface-level particle concentrations. Therefore, ground-based measurements and comparisons with satellite-based aerosol data can lower the uncertainty associated with local and external aerosols, and meteorological parameters (Kumar et al., 2007).

The use of low-cost sensors in surface measurements can alleviate the limitations of human resources and can enable setting up multiple ground-based measurement stations easier, regardless of a lack of funding and/or technical expertise. A correlative analysis based on satellite-based aerosol optical depth (AOD) data and ground-level particle concentration can be conducted to evaluate the proposed method for estimating PM2.5 in areas that lack ground-level observations (van Donkelaar et al., 2015).

Several studies have examined the air quality and aerosol loading trend over the Kathmandu Valley (Aryal et al., 2009; Islam et al., 2020; Mahapatra et al., 2019). MODIS-equipped Earth observation satellites Terra and Aqua pass over the Kathmandu valley every morning and afternoon (at about 10:30 am and 1:00 pm local time, respectively), and MODIS' AOD data (3 km × 3 km) can be used to compare with surface level PM2.5 (Ramachandran and Kedia, 2013).

This paper will present and analyze the hourly variability of PM2.5 observed by PurpleAir monitor at the Pulchowk Engineering Campus (TU), located about a hundred meters from the bus station, for each season and compare surface-level particle concentrations with satellite-based AODs using relative humidity correction factor. Relative humidity influences aerosol particle growth and refractive index, affecting aerosol mass concentration (Altaratz et al., 2013; Hagan and Kroll, 2020; Malm et al., 2000). Kathmandu valley's bowl-shaped structure and surrounding mountains make it a unique case study for investigating air pollution (Kitada and Regmi, 2003; Panday and Prinn, 2009; Shakya et al., 2017; Shrestha et al., 2017). This paper also presents the local and regional aspects of long-range air pollution transport over Kathmandu Valley using cluster analysis of back trajectory air masses arriving over the observation site.

 
2 MATERIALS AND METHODS


 
2.1 Site Description

A PurpleAir sensor was deployed in the Kathmandu valley (Pulchowk Engineering Campus, IOE, Lalitpur, Lat. 27.68°N, Long. 85.31°E, and Alt. 1350 m Fig. 1) to measure real-time PM2.5 concentration data. The city of Kathmandu is Nepal's largest metropolitan area and is highly polluted. Nepal is surrounded by the Indo-Gangetic Plain in the south and the large Himalayas in the north (Shakya et al., 2017; Regmi et al., 2020). High mountains surround the Kathmandu valley, ranging from 2000 meters to 2800 meters, and the valley is shaped like a bowl, trapping pollution.

Fig. 1. As seen in the MODIS image (taken by NASA's Terra satellite on February 9, 2020), there is a noticeable accumulation of haze on the foothills of the Himalayan range. The top right corner of the figure shows a PurpleAir monitor located at Kathmandu Valley.Fig. 1. As seen in the MODIS image (taken by NASA's Terra satellite on February 9, 2020), there is a noticeable accumulation of haze on the foothills of the Himalayan range. The top right corner of the figure shows a PurpleAir monitor located at Kathmandu Valley.

This city is experiencing rapid urbanization and population growth. The region is characterized by haphazard construction, unmanaged industries, brick and kiln production, and solid waste and biomass burning. Kathmandu also experiences significant amount of transboundary air pollution (Kitada and Regmi, 2003). Transported dust aerosols impact the cloud microphysical properties significantly. As a result of biomass burning and dust storms in the Indo-Gangetic Plain region, and long-range transportation, aerosols accumulate over Nepal, which, when combined with local pollution sources, contributes to high levels of pollution (Adhikari and Mejia, 2022; Becker et al., 2021; Das et al., 2021; Jethva et al., 2019; Regmi et al., 2020).

 
2.2 PurpleAir, Sampling Methods and MODIS AOD

PurpleAir's website (https://www2.purpleair.com/) provides real-time PM2.5 (in µg m–3) data. Microprocessor-based circuits are used to calculate equivalent particle diameters and the number of particles with different diameters per unit volume (Ouimette et al., 2022; Yong, 2016). The processing algorithms for the Plan-tower PMS5003 sensors (PA-PMS) used in the PurpleAir (PA) monitor configuration are provided in He et al. (2020). The remainder of this paper uses the symbol PM2.5(PA) for PM2.5 observed by a PurpleAir monitor. Additionally, we compare PM2.5(PA) data with PM2.5(US) data to indicate the reliability of PurpleAir. PM2.5(US) is monitored by the Ambient Air Quality Monitoring Station (Beta Attenuation Monitor, BAM) at the Phora Durbar Recreation Center, 3 km from the PurpleAir monitoring site and supported by the United States Embassy (Edwards et al., 2021). The BAM 1022 measures and records airborne PM concentrations in µg m–3 at local temperatures and atmospheric pressures by utilizing beta-ray attenuation (Magi et al., 2020). The manufacturer's website provides a complete description of the BAM 1022's operation (https://metone.com/air-quality-particulate-measurement/regulatory/bam-1022/).

A significant correlation exists between PM2.5(PA) and PM2.5(US) data sets with a coefficient of determination (R2) of 0.98. The best fit line equation is PM2.5(PA) = 0.93(± 0.04) PM2.5(US) + 13.23 (± 2.40) as shown in Fig. 2.

Fig. 2. The scatter plot shows the monthly averages for PM2.5(PA) and PM2.5(US) in unit of µg m–3. Based on the legend in the figure, the symbols are ordered from higher to lower PM2.5 for the different months. December month is discarded for comparison due to a lack of data availability.Fig. 2. The scatter plot shows the monthly averages for PM2.5(PA) and PM2.5(US) in unit of µg m–3. Based on the legend in the figure, the symbols are ordered from higher to lower PM2.5 for the different months. December month is discarded for comparison due to a lack of data availability.

However, the two sets are not validated individually against other nearby reference optical devices. In Fig. 2, the scatter plot shows that PurpleAir data PM2.5(PA) near the bus station is slightly above the 1:1 line compared to PM2.5(US) at Phora Durbar and shows that PM2.5(PA) provides usable data representing the atmosphere of Kathmandu valley.

In this study, PM2.5(PA) is compared with NASA's MODIS column integrated Aerosol Optical Depth (AOD at 550 nm) product, which is corrected for relative humidity. The rest of this paper uses AOD550nm to represent AOD retrieved from Satellite data. AOD550nm aerosol products collected from MODIS Aqua and Terra at 3 km × 3 km is analyzed in this study. The 3 km × 3 km algorithm differs from the 10 km × 10 km algorithm simply in the way reflectance pixels are ingested, organized, and selected (Levy et al., 2013). PurpleAir provides optical data matching the products of aerosol optical devices such as cell reciprocal and TSI 3563 integrating nephelometers, according to Calvello et al. (2008) and Ouimette et al. (2022).

 
2.4 Model Description

The relationship between PM and AOD is illustrated by a simplified linear equation with a relative humidity correction factor. To express a statistical model, we have written the particle concentration (PM) in general, observed at the ground level with the dry sample, which can be expressed quantitatively (Xu and Zhang, 2020) as

 

ndry(r) denotes the number of particles per unit volume in atmospheric space per unit particle radius and ρ is the aerosol particle density. Based on the hypothesis of spherical particles, the columnar AOD can be calculated using the Mie scattering theory with the equation (Calvello et al., 2008), and can be written as,

  

where r is the radius of the assumed spherical particles, Qext is the extinction efficiency factor defined by van de Hulst (1981), and namb (r, h) represents the aerosol size distribution giving a concentration of particles per unit volume per particle radius at height h, which is factorized in two parts based on height.

 

where n0 is the particle concentration at the surface level and H is the aerosol scale height, then the equation for AOD can be expressed as given by Calvello et al. (2008).

 

Several models explain the relationship between f(RH) and Relative humidity (RH) (Brock et al., 2016, 2021; Chen et al., 2016; Kasten, 1969; Kotchenruther and Hobbs, 1998; Zheng et al., 2017). This work utilizes the f(RH) expression (Brock et al., 2021; Zheng et al., 2017) to examine aerosol light extinction induced by hygroscopic growth.

 

where AODdry represents the aerosol optical depth with dehydration adjustment. Then the expression for AOD in terms of the dry condition can be expressed as,

 

To investigate the AOD vs. PM relation, the size distribution and extinction efficiency <Qext> and effective aerosol radius reff are expressed (Xu and Zhang, 2020) as,

 

And, the effective radius of aerosol particles is given as,

 

Based on the above Eqs. (2), (4), (7), and (8) a relation between AOD/f(RH) and PM can be obtained as

 

Thus Eq. (9) provides, assuming H as a constant for all data for this study, f(RH) as the correcting factor to study the correlation between AOD/(f(RH)) vs. PM.

 
2.4 Statistical Analysis

AOD550nm obtained from two satellite measurements, Aqua and Terra, is compared with the hourly averaged PurpleAir data PM2.5(PA). For comparison with ground based PM2.5(PA), the AOD550nm are corrected by dividing with f(RH), where RH is ambient relative humidity. At different humidity levels, aerosol particles with different chemical compositions but the same mass concentration exhibit different aerosol optical properties (Jin et al., 2022). Accordingly, this correlation study investigates the correlation between aerosol extinction and mass concentration using relative humidity correction factor, f(RH).

MOD04 and MYD04_3K data (MOD04 for Terra, MYD04 for Aqua), 3 km × 3 km AOD550nm data were extracted and correlated with a temporal variation of PM2.5(PA) from PurpleAir sensors at ground level. AOD550nm measurements from two satellites, Aqua and Terra, were observed simultaneously with PM2.5(PA) measurements. We performed the graphical analysis using the Levenberg-Marquardt least orthogonal distance method implemented in IGOR (https://www.​wavemetrics.com/). The data were then analyzed using linear regression analysis, and a coefficient of determination (R2) was obtained. A two-tailed P statistic and a coefficient of correlation coefficient (R) was calculated to determine the significance of the correlation. Data with confidence levels (based on P values) < 95% were disregarded for statistical significance.

 
2.5 Cluster Analysis

The aerosol particle trajectories arriving at Kathmandu at 500 m, 1000 m, and 1500 m asl were analyzed using cluster analysis to understand the origin of aerosol particles over Kathmandu Valley's vertical column. For each season, seasonal clusters were generated by analyzing five days of air mass trajectories starting at 500 m, 1000 m, and 1500 m asl over Kathmandu (Lat. 27.68°N, Long. 85.31°E) at 0:00, 6:00, 12:00, and 18:00 UTC each day. In this study, the cluster calculations were conducted using the free software Traj-stat (Regmi et al., 2020; Wang, 2014).

 
3 RESULTS AND DISCUSSION


 
3.1 The Hourly Variation of the Seasonally Averaged PM2.5

Fig. 3 shows hourly variations of PM2.5(PA) concentrations (in µg m3) for each season in 2020. The average maximum PM2.5(PA) is observed to be 101 ± 26.31 µg m3 in the winter season, while the minimum is seen in summer (22.78 ± 3.23 µg m3). Similarly, the average concentration of 55.58 ± 11.42 µg m3 is observed in the spring and 45.46 ± 12.16 µg m3 in the autumn. The ± 1 s standard deviation obtained from hourly averaged measurements for each season are given adjacent to the seasonal mean values. Kathmandu is a bowl-shaped urban basin in Nepal, and during winter, the planetary boundary layer is thinner due to the dense, cooler air near the surface. A layer of cooler air sits beneath the warm air above, forming a kind of lid in the atmosphere referred to as winter inversion. Only within this layer does vertical air mixing occur, so pollutants cannot disperse in the atmosphere. Kathmandu relies mainly on wood for heating in winter, and wood burning contributes significantly to air pollution. Kotchenruther (2020) found elevated levels of PM2.5 during the winter in the Northwest U.S. According to the study, residential wood combustion, motor vehicle emissions, gaseous NOx emissions, and particulate sulfate emissions are the primary sources of PM2.5 during winter.

Fig. 3. Hourly averaged (at local time) averaged particle concentration (PM2.5(PA)) in µg m–3 for each season by using the corresponding months' data for each season.Fig. 3. Hourly averaged (at local time) averaged particle concentration (PM2.5(PA)) in µg m–3 for each season by using the corresponding months' data for each season.

For all seasons, particle mass concentrations are bimodal, significantly higher in the mornings (around 8:00 am) and evenings (around 7:00 pm). Fig. 3 shows the seasonal variations of hourly averaged PM2.5(PA), which are bimodal in all seasons. The amplitude and width of PM2.5(PA) is maximum during the winter seasons, and the patterns are similar during spring, summer, and autumn. No significant variation in hourly averaged PM2.5(PA) is seen during summer. A signature of local activity can be seen in PM2.5(PA), such as during the household cooking and traffic emissions time. Many studies have shown that household and commercial cooking significantly contribute to PM2.5 pollution in urban areas (Balasubramanian et al., 2021; Pervez et al., 2019; Robinson et al., 2018). Earlier studies have also shown that traffic and cooking contribute to air pollution in Kathmandu valley during rush hours (morning and evening) and off hours in the afternoon (Islam et al., 2020).

PM2.5(PA) increases gradually during rush hours in traffic. On a global scale, cooking and heating fuels contribute 20–55% to anthropogenic particle emissions (Balasubramanian et al., 2021; Pervez et al., 2019). The study concludes that domestic cooking and traffic are significant anthropogenic factors affecting the Kathmandu valley's surface-level PM2.5(PA) concentrations. Previously, it has been shown that traffic contributes significantly to air pollution in different urban areas, with coarse particles originating from non-exhaust sources like road abrasion, brake wear, and tire wear, while fine particles are emitted directly from fuel combustion (Kumar and Goel, 2016; Pant and Harrison, 2013). Low temperatures can lead to the formation of secondary aerosols during the winter months (Duan et al., 2020; Mues et al., 2018). Previous studies have shown the importance of secondary organic aerosols (SOA) in contributing to PM2.5 (Bui et al., 2022; Mancilla et al., 2015).

Fig. 3 also shows that the average particle concentration at hourly averaged PM2.5(PA) decreases from winter to summer, then increases again in autumn as the seasons change. A rise in temperature in the summer causes surface-level particles to diffuse rapidly in the vertical atmosphere, resulting in lower surface concentrations. PurpleAir record shows an average temperature of 17.77 ± 4.00°C in winter, 24.08 ± 2.65°C in summer, and 28.96 ± 1.65°C in autumn. The summer also brings significant amounts of rain, which helps removal of aerosol particles from the atmosphere (Becker et al., 2021). June to September are rainy monsoon months in Kathmandu, which can significantly reduce air pollution. Thus, the monsoon rain also plays a crucial role in reducing the surface level air pollution in Kathmandu in the summer months from June to September. The COVID lockdown period from March to July also significantly reduced traffic flow, which might have affected particle concentrations in the atmosphere (Baral and Thapa, 2021).

 
3.2 Comparison of MODIS AOD with PM2.5(PA)

Fig. 4 illustrates the correlation between Aqua and Terra AOD550nm and reveals R = 0.942, R2 = 0.888, and P < 0.005. The calibration of two satellite sensors and the retrieval algorithm for MODIS AOD550nm during spatial observations may cause some deviations in the AOD data collected by the two satellites (Green et al., 2009; Gupta et al., 2020; Ichoku et al., 2005; Koelemeijer et al., 2006). However, a significant high correlation indicates that AOD550nm by two satellite measurements can represent reliable and comparable aerosol optical depth, and it can be used for daily averages and combined to compare with other concurrently observed aerosol concentrations such as PM2.5(PA).

 Fig. 4. Scatter plot of MODIS AOD550nm (Terra) vs. MODIS AOD550nm (Aqua) comparing the same day data observed from two satellites in the morning and afternoon.Fig. 4. Scatter plot of MODIS AOD550nm (Terra) vs. MODIS AOD550nm (Aqua) comparing the same day data observed from two satellites in the morning and afternoon.

Fig. 5 presents a comparison of the hourly average of PM2.5(PA) with the AOD data observed by two satellites at 550 nm wavelength, AOD550nm, without relative humidity correction factor. Fig. 5 also shows the standard deviation and linear best fit line. The best fit line provides an equation, AOD550nm = 0.038(± 0.037) + (0.0068 ± 0.0006) PM2.5 (PA), and the correlation coefficient (R = 0.638, R2 = 0.407) and P-values (<< 0.005) are also obtained from the plot. Based on the correlation between these two parameters, Fig. 5 indicates that surface air pollution, PM2.5(PA), significantly influences aerosol optical depth (AOD550nm).

Fig. 5. A comparison between Aqua and Terra Satellites' spatially averaged AOD at 550 nm (AOD550nm) and PurpleAir Monitor's hourly average PM2.5(PA) around the satellite overpass.Fig. 5. A comparison between Aqua and Terra Satellites' spatially averaged AOD at 550 nm (AOD550nm) and PurpleAir Monitor's hourly average PM2.5(PA) around the satellite overpass.

According to Table 1, the correlation coefficients between AOD550nm and corresponding PM2.5(PA) are improved by dividing AOD550nm by f(RH). In seasons of low temperature, f(RH) has a greater effect on the R2 than in seasons of higher temperatures. As a result of the temperature inversion at low temperatures, particle concentrations can remain close to the surface due to the significant effect of vertical column AOD correction with f(RH). In low-temperature months, aerosol particles will distribute uniformly near the surface. In summer, however, very few AOD550nm coincide with the corresponding hourly averaged PM2.5(PA) due to persistent monsoon clouds. Correlation results obtained in summer are also given in the table which, however, are less reliable owing to a smaller number of data points on account of monsoon clouds.

Table 1. The correlation coefficients for each seasons using the available one-year 2020 data without and with the f(RH) corrected AOD data vs. PM2.5(PA).

Based on the variations in correlation coefficients, meteorological parameters such as relative humidity should be considered when retrieving aerosol optical depths from satellite data (Zhang et al., 2009).

 
3.3 Seasonal Cluster Analysis

Fig. 6 and supplementary figures Fig. S1 and Fig. S2 show the seasonal clusters of five-day back trajectories over Kathmandu Valley during four seasons at 500 meters, 1000 meters and 1500 meters.

Fig. 6. An analysis of five-day air mass back trajectories computed with Analysis NOAA HYSPLITT and HYSPLIT model reaching Kathmandu at 500 m at different seasons. For each season’s cluster analysis, the percentage contribution is presented.Fig. 6. An analysis of five-day air mass back trajectories computed with Analysis NOAA HYSPLITT and HYSPLIT model reaching Kathmandu at 500 m at different seasons. For each season’s cluster analysis, the percentage contribution is presented.

According to percentage contributions, three predominant air masses reach over the Kathmandu valley every season, with five clusters exhibiting the best representation. The cluster analysis shows that fast-moving air masses contribute less, ranging from 1 to 7 percent, to air pollution at the observation site.

At 500 meters, 44% of the winter air mass comes from the Indo-Gangetic plain (IGP), Nepal's south, 38% from the southwest, 16% from the west, and 2% from the west (Fig. 6). The IGP is densely populated, and its aerosol loading is high due to industrial and urban pollution, dust, biomass burning, and the flat land of southern Nepal that lines its northern edge (Regmi et al., 2020). Fig. 6 illustrates the dominance of southerly and southwesterly air masses in winter (December–February) and spring (March–May). The trajectory also indicates that south-easterly air masses from the Bay of Bengal contribute significantly and prominently in summer (June–August), whereas southerly air masses dominate in autumn (September–November). According to supplementary figures, Fig. S1 and Fig. S2, the dominant clusters at 1000 meters and 1500 meters are not significantly different from those at 500 meters. Therefore, the air pollution at the observation site is transported vertically from a similar direction to that of 500 meters. At different altitudes, the air mass trajectory crosses the IGP region on its way to the observation site, Kathmandu Valley. As a result, air pollution over the IGP region mainly impacts the aerosol optical depth and the overall vertical air pollution concentration.

 
4 CONCLUSIONS


The PurpleAir sensor was installed in the Kathmandu Valley, a Himalayan foothill, to determine its capability to measure surface-level small-size particles whose aerodynamic diameter is equal to or smaller than 2.5 micrometers (PM2.5). The observation shows that the bimodal particulate concentrations (PM2.5) peak at about 8 am and 7 pm, coincident with the density of traffic and other anthropogenic pollutants events, such as cooking and heating, which indicates Kathmandu has significant local sources of outdoor air pollution. In winter, temperature inversion causes particles to remain close to the surface rather than disperse vertically, showing the highest concentrations. During the summer, which is monsoon season, PM2.5 levels are low and bimodal variations are smaller. PurpleAir's surface-level PM2.5 and MODIS's AOD measurements correlate better with the relative humidity correction factor. For further analyzing the comparison between PM2.5 and AOD, boundary layer height (BLH) data can be used, but these data are not available for this study. An air mass cluster analysis shows that transboundary air pollution over different altitudes at Kathmandu valley is received from different directions and source regions. The contribution of directional airmass varies with the season. The Indo-Gangetic Plain pollutes the atmosphere over Kathmandu during winter, spring, and autumn. In addition to contributing to aerosols over the atmospheric column and, eventually, to column integrated AOD550nm, transboundary air pollution might bias the comparison between PM2.5(PA) and AOD550nm. Moreover, the study establishes that simultaneous measurements of particle concentration at the surface level and AOD over a vertical column allow for the extrapolation of surface-level concentrations using satellite-based AOD, thereby, calling for a more robust and comprehensive analysis of spatial and temporal distribution of PM2.5 concentration characteristics and columnar aerosol optical depth.

 
ACKNOWLEDGMENTS


The authors acknowledge NOAA-ARL and NASA for providing HYSPLIT air mass back trajectories, MODIS satellite air quality data, and Franklin Pierce University for providing the PurpleAir monitor.

 
DISCLOSURE


Any reference to companies or specific commercial products does not constitute a recommendation, support, or endorsement and we have no conflict of interest to disclose.


REFERENCES


  1. Adhikari, P., Mejia, J.F. (2022). Impact of transported dust aerosols on precipitation over the Nepal Himalayas using convection-permitting WRF-Chem simulation. Atmos. Environ.: X 15, 100179. https://doi.org/10.1016/j.aeaoa.2022.100179

  2. Altaratz, O., Bar-Or, R.Z., Wollner, U., Koren, I. (2013). Relative humidity and its effect on aerosol optical depth in the vicinity of convective clouds. Environ. Res. Lett. 8, 034025. https://doi.org/​10.1088/1748-9326/8/3/034025

  3. Ardon-Dryer, K., Dryer, Y., Williams, J.N., Moghimi, N. (2020). Measurements of PM2.5 with PurpleAir under atmospheric conditions. Atmos. Meas. Tech. 13, 5441–5458. https://doi.org/​10.5194/amt-13-5441-2020

  4. Aryal, R.K., Lee, B.K., Karki, R., Gurung, A., Baral, B., Byeon, S.H. (2009). Dynamics of PM2.5 concentrations in Kathmandu Valley, Nepal. J. Hazard. Mater. 168, 732–738. https://doi.org/​10.1016/j.jhazmat.2009.02.086

  5. Aryal, R.P., Voss, K.J., Terman, P.A., Keene, W.C., Moody, J.L., Welton, E.J., Holben, B.N. (2014). Comparison of surface and column measurements of aerosol scattering properties over the western North Atlantic Ocean at Bermuda. Atmos. Chem. Phys. 14, 7617–7629. https://doi.org/​10.5194/acp-14-7617-2014

  6. Balasubramanian, S., Domingo, N.G.G., Hunt, N.D., Gittlin, M., Colgan, K.K., Marshall, J.D., Robinson, A.L., Azevedo, I.M.L., Thakrar, S.K., Clark, M.A., Tessum, C.W., Adams, P.J., Pandis, S.N., Hill, J.D. (2021). The food we eat, the air we breathe: A review of the fine particulate matter-induced air quality health impacts of the global food system. Environ. Res. Lett. 16, 103004. https://doi.org/10.1088/1748-9326/ac065f

  7. Baral, B.D., Thapa, K. (2021). Effect of the COVID-19 lockdown on ambient air quality in major cities of Nepal. J. Health Pollut. 11, 210211. https://doi.org/10.5696/2156-9614-11.29.210211

  8. Becker, S., Sapkota, R.P., Pokharel, B., Adhikari, L., Pokhrel, R.P., Khanal, S., Giri, B. (2021). Particulate matter variability in Kathmandu based on in-situ measurements, remote sensing, and reanalysis data. Atmos. Res. 258, 105623. https://doi.org/10.1016/j.atmosres.2021.105623

  9. Brock, C.A., Wagner, N.L., Anderson, B.E., Attwood, A.R., Beyersdorf, A., Campuzano-Jost, P., Carlton, A.G., Day, D.A., Diskin, G.S., Gordon, T.D., Jimenez, J.L., Lack, D.A., Liao, J., Markovic, M.Z., Middlebrook, A.M., Ng, N.L., Perring, A.E., Richardson, M.S., Schwarz, J.P., Washenfelder, R.A., et al. (2016). Aerosol optical properties in the southeastern United States in summer – Part 1: Hygroscopic growth. Atmos. Chem. Phys. 16, 4987–5007. https://doi.org/10.5194/acp-16-4987-2016

  10. Brock, C.A., Froyd, K.D., Dollner, M., Williamson, C.J., Schill, G., Murphy, D.M., Wagner, N.J., Kupc, A., Jimenez, J.L., Campuzano-Jost, P., Nault, B.A., Schroder, J.C., Day, D.A., Price, D.J., Weinzierl, B., Schwarz, J.P., Katich, J.M., Wang, S., Zeng, L., Weber, R., et al. (2021). Ambient aerosol properties in the remote atmosphere from global-scale in situ measurements. Atmos. Chem. Phys. 21, 15023–15063. https://doi.org/10.5194/acp-21-15023-2021

  11. Bui, T.H., Nguyen, D.L., Nguyen, H.H. (2022). Study of aerosol optical properties at two urban areas in the north of Vietnam with the implication for biomass burning impacts. Environ. Sci. Pollut. Res. 29, 41923–41940. https://doi.org/10.1007/s11356-021-15608-5

  12. Calvello, M., Esposito, F., Leone, L., Pavese, G., Restieri, R. (2008). High resolution measurement of aerosol equivalent scale heigth over wide range. 16, 443–448.

  13. Chen, P., Li, C., Kang, S., Yan, F., Zhang, Q., Ji, Z., Tripathee, L., Rupakheti, D., Rupakheti, M., Qu, B., Sillanpää, M. (2016). Source apportionment of particle-bound polycyclic aromatic hydrocarbons in Lumbini, Nepal by using the positive matrix factorization receptor model. Atmos. Res. 182, 46–53. https://doi.org/10.1016/j.atmosres.2016.07.011

  14. Das, M., Das, A., Sarkar, R., Mandal, P., Saha, S., Ghosh, S. (2021). Exploring short term spatio-temporal pattern of PM2.5 and PM10 and their relationship with meteorological parameters during COVID-19 in Delhi. Urban Clim. 39, 100944. https://doi.org/10.1016/j.uclim.2021.100944

  15. Devi, N.L., Kumar, A., Yadav, I.C. (2020). PM10 and PM2.5 in Indo-Gangetic Plain (IGP) of India: Chemical characterization, source analysis, and transport pathways. Urban Clim. 33, 100663. https://doi.org/10.1016/j.uclim.2020.100663

  16. Di, Q., Wang, Y., Zanobetti, A., Wang, Y., Koutrakis, P., Choirat, C., Dominici, F., Schwartz, J.D. (2017). Air Pollution and Mortality in the Medicare Population. N. Engl. J. Med. 376, 2513–2522. https://doi.org/10.1056/nejmoa1702747

  17. Duan, J., Huang, R.J., Li, Y., Chen, Q., Zheng, Y., Chen, Y., Lin, C., Ni, H., Wang, M., Ovadnevaite, J., Ceburnis, D., Chen, C., Worsnop, D.R., Hoffmann, T., O’Dowd, C., Cao, J. (2020). Summertime and wintertime atmospheric processes of secondary aerosol in Beijing. Atmos. Chem. Phys. 20, 3793–3807. https://doi.org/10.5194/acp-20-3793-2020

  18. Dusek, U., Frank, G.P., Hildebrandt, L., Curtius, J., Schneider, J., Walter, S., Chand, D., Drewnick, F., Hings, S., Jung, D., Borrmann, S., Andreae, M.O. (2006). Size matters more than chemistry for cloud-nucleating ability of aerosol particles. Science 312, 1375–1378. https://doi.org/​10.1126/science.1125261

  19. Edwards, L., Rutter, G., Iverson, L., Wilson, L., Chadha, T.S., Wilkinson, P., Milojevic, A. (2021). Personal exposure monitoring of PM2.5 among US diplomats in Kathmandu during the COVID-19 lockdown, March to June 2020. Sci. Total Environ. 772, 144836. https://doi.org/10.1016/j.​scitotenv.2020.144836

  20. Green, M., Kondragunta, S., Ciren, P., Xu, C. (2009). Comparison of GOES and MODIS aerosol optical depth (AOD) to aerosol robotic network (AERONET) AOD and IMPROVE PM2.5 mass at Bondville, Illinois. J. Air Waste Manage. Assoc. 59, 1082–1091. https://doi.org/10.3155/1047-3289.59.9.1082

  21. Gupta, P., Remer, L.A., Patadia, F., Levy, R.C., Christopher, S. A. (2020). High-resolution gridded level 3 aerosol optical depth data from MODIS. Remote Sens. 12, 2847. https://doi.org/​10.3390/rs12172847

  22. Hagan, D.H., Kroll, J.H. (2020). Assessing the accuracy of low-cost optical particle sensors using a physics-based approach. Atmos. Meas. Tech. 13, 6343–6355. https://doi.org/10.5194/amt-13-6343-2020

  23. Haywood, J., Boucher, O. (2000). Estimates of the direct and indirect radiative forcing due to tropospheric aerosols: A review. Rev. Geophys. 38, 513–543. https://doi.org/10.1029/​1999RG000078

  24. He, M., Kuerbanjiang, N., Dhaniyala, S. (2020). Performance characteristics of the low-cost Plantower PMS optical sensor. Aerosol Sci. Technol. 54, 232–241. https://doi.org/10.1080/​02786826.2019.1696015

  25. Ichoku, C., Remer, L.A., Eck, T.F. (2005). Correction to “Quantitative evaluation and intercomparison of morning and afternoon Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol measurements from Terra and Aqua.” J. Geophys. Res. 110, D10S99. https://doi.org/10.1029/2005JD005897

  26. Islam, M.R., Jayarathne, T., Simpson, I.J., Werden, B., Maben, J., Gilbert, A., Praveen, P.S., Adhikari, S., Panday, A.K., Rupakheti, M., Blake, D.R., Yokelson, R.J., DeCarlo, P.F., Keene, W.C., Stone, E.A. (2020). Ambient air quality in the Kathmandu Valley, Nepal, during the pre-monsoon: Concentrations and sources of particulate matter and trace gases. Atmos. Chem. Phys. 20, 2927–2951. https://doi.org/10.5194/acp-20-2927-2020

  27. Jethva, H., Torres, O., Field, R.D., Lyapustin, A., Gautam, R., Kayetha, V. (2019). Connecting crop productivity, residue fires, and air quality over Northern India. Sci. Rep. 9, 16594. https://doi.org/10.1038/s41598-019-52799-x

  28. Jin, X., Li, Z., Wu, T., Wang, Y., Cheng, Y., Su, T., Wei, J., Ren, R., Wu, H., Li, S., Zhang, D., Cribb, M. (2022). The different sensitivities of aerosol optical properties to particle concentration, humidity, and hygroscopicity between the surface level and the upper boundary layer in Guangzhou, China. Sci. Total Environ. 803, 150010. https://doi.org/10.1016/j.scitotenv.2021.​150010

  29. Karagulian, F., Belis, C.A., Dora, C.F.C., Prüss-Ustün, A.M., Bonjour, S., Adair-Rohani, H., Amann, M. (2015). Contributions to cities’ ambient particulate matter (PM): A systematic review of local source contributions at global level. Atmos. Environ. 120, 475–483. https://doi.org/​10.1016/j.atmosenv.2015.08.087

  30. Kasten, F. (1969). Visibility forecast in the phase of pre-condensation. Tellus A 21, 631. https://doi.org/10.3402/tellusa.v21i5.10112

  31. Kitada, T., Regmi, R.P. (2003). Dynamics of air pollution transport in late wintertime over Kathmandu Valley, Nepal: As revealed with numerical simulation. J. Appl. Meteorol. 42, 1770–1798. https://doi.org/10.1175/1520-0450(2003)042<1770:DOAPTI>2.0.CO;2

  32. Koelemeijer, R.B.A., Homan, C.D., Matthijsen, J. (2006). Comparison of spatial and temporal variations of aerosol optical thickness and particulate matter over Europe. Atmos. Environ. 40, 5304–5315. https://doi.org/10.1016/j.atmosenv.2006.04.044

  33. Kotchenruther, R.A., Hobbs, P.V. (1998). Humidification factors of aerosols from biomass burning in Brazil. J. Geophys. Res. 103, 32081–32089. https://doi.org/10.1029/98JD00340

  34. Kotchenruther, R.A. (2020). Recent changes in winter PM2.5 contributions from wood smoke, motor vehicles, and other sources in the Northwest U.S. Atmos. Environ. 237, 117724. https://doi.org/10.1016/j.atmosenv.2020.117724

  35. Kumar, N., Chu, A., Foster, A. (2007). An empirical relationship between PM2.5 and aerosol optical depth in Delhi Metropolitan. Atmos. Environ. 41, 4492–4503. https://doi.org/10.1016/j.​atmosenv.2007.01.046

  36. Kumar, A., Singh, N., Anshumali, Solanki, R. (2018). Evaluation and utilization of MODIS and CALIPSO aerosol retrievals over a complex terrain in Himalaya. Remote Sens. Environ. 206, 139–155. https://doi.org/10.1016/j.rse.2017.12.019

  37. Kumar, P., Goel, A. (2016). Concentration dynamics of coarse and fine particulate matter at and around signalised traffic intersections. Environ. Sci.: Processes Impacts 18, 1220–1235. https://doi.org/10.1039/C6EM00215C

  38. Levy, R.C., Mattoo, S., Munchak, L.A., Remer, L.A., Sayer, A.M., Patadia, F., Hsu, N.C. (2013). The Collection 6 MODIS aerosol products over land and ocean. Atmos. Meas. Tech. 6, 2989–3034. https://doi.org/10.5194/amt-6-2989-2013

  39. Lim, S.S., Vos, T., Flaxman, A.D., Danaei, G., Shibuya, K., Adair-Rohani, H., AlMazroa, M.A., Amann, M., Anderson, H.R., Andrews, K.G., Aryee, M., Atkinson, C., Bacchus, L.J., Bahalim, A.N., Balakrishnan, K., Balmes, J., Barker-Collo, S., Baxter, A., Bell, M.L., Blore, J.D., et al. (2012). A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: A systematic analysis for the Global Burden of Disease Study 2010. Lancet 380, 2224–2260. https://doi.org/10.1016/S0140-6736(12)61766-8

  40. Magi, B.I., Cupini, C., Francis, J., Green, M., Hauser, C. (2020). Evaluation of PM2.5 measured in an urban setting using a low-cost optical particle counter and a Federal Equivalent Method Beta Attenuation Monitor. Aerosol Sci. Technol. 54, 147–159. https://doi.org/10.1080/02786826.​2019.1619915

  41. Mahapatra, P.S., Puppala, S.P., Adhikary, B., Shrestha, K.L., Dawadi, D.P., Paudel, S.P., Panday, A.K. (2019). Air quality trends of the Kathmandu Valley: A satellite, observation and modeling perspective. Atmos. Environ. 201, 334–347. https://doi.org/10.1016/j.atmosenv.2018.12.043

  42. Malm, W.C., Day, D.E., Kreidenweis, S.M. (2000). Light scattering characteristics of aerosols as a function of relative humidity: Part I—A comparison of measured scattering and aerosol concentrations using the theoretical models. J. Air Waste Manage. Assoc. 50, 686–700. https://doi.org/10.1080/10473289.2000.10464117

  43. Mancilla, Y., Herckes, P., Fraser, M.P., Mendoza, A. (2015). Secondary organic aerosol contributions to PM2.5 in Monterrey, Mexico: Temporal and seasonal variation. Atmos. Res. 153, 348–359. https://doi.org/10.1016/j.atmosres.2014.09.009

  44. Moody, J.L., Keene, W.C., Cooper, O.R., Voss, K.J., Aryal, R., Eckhardt, S., Holben, B., Maben, J.R., Izaguirre, M.A., Galloway, J.N. (2014). Flow climatology for physicochemical properties of dichotomous aerosol over the western North Atlantic Ocean at Bermuda. Atmos. Chem. Phys. 14, 691–717. https://doi.org/10.5194/acp-14-691-2014

  45. Morawska, L., Thai, P.K., Liu, X., Asumadu-Sakyi, A., Ayoko, G., Bartonova, A., Bedini, A., Chai, F., Christensen, B., Dunbabin, M., Gao, J., Hagler, G.S.W., Jayaratne, R., Kumar, P., Lau, A.K.H., Louie, P.K.K., Mazaheri, M., Ning, Z., Motta, N., Mullins, B., et al. (2018). Applications of low-cost sensing technologies for air quality monitoring and exposure assessment: How far have they gone? Environ. Int. 116, 286–299. https://doi.org/10.1016/j.envint.2018.04.018

  46. Mues, A., Lauer, A., Lupascu, A., Rupakheti, M., Kuik, F., Lawrence, M.G. (2018). WRF and WRF-Chem v3.5.1 simulations of meteorology and black carbon concentrations in the Kathmandu Valley. Geosci. Model Dev. 11, 2067–2091. https://doi.org/10.5194/gmd-11-2067-2018

  47. Munir, S., Mayfield, M., Coca, D., Jubb, S.A., Osammor, O. (2019). Analysing the performance of low-cost air quality sensors, their drivers, relative benefits and calibration in cities—A case study in Sheffield. Environ. Monit. Assess. 191, 94. https://doi.org/10.1007/s10661-019-7231-8

  48. Ouimette, J.R., Malm, W.C., Schichtel, B.A., Sheridan, P.J., Andrews, E., Ogren, J.A., Arnott, W.P. (2022). Evaluating the PurpleAir monitor as an aerosol light scattering instrument. Atmos. Meas. Tech. 15, 655–676. https://doi.org/10.5194/amt-15-655-2022

  49. Panday, A.K., Prinn, R.G. (2009). Diurnal cycle of air pollution in the Kathmandu Valley, Nepal: Observations. J. Geophys. Res. 114, D09305. https://doi.org/10.1029/2008JD009777

  50. Pant, P., Harrison, R.M. (2013). Estimation of the contribution of road traffic emissions to particulate matter concentrations from field measurements: A review. Atmos. Environ. 77, 78–97. https://doi.org/10.1016/j.atmosenv.2013.04.028

  51. Pervez, S., Verma, M., Tiwari, S., Chakrabarty, R.K., Watson, J.G., Chow, J.C., Panicker, A.S., Deb, M.K., Siddiqui, M.N., Pervez, Y.F. (2019). Household solid fuel burning emission characterization and activity levels in India. Sci. Total Environ. 654, 493–504. https://doi.org/10.1016/j.scitotenv.​2018.11.019

  52. Ramachandran, S., Kedia, S. (2013). Aerosol optical properties over South Asia from ground-based observations and remote sensing: A review. Climate 1, 84–119. https://doi.org/​10.3390/cli1030084

  53. Ramanathan, V., Crutzen, P.J., Kiehl, J.T., Rosenfeld, D. (2001). Aerosols, climate, and the hydrological cycle. Science 294, 2119–2124. https://doi.org/10.1126/science.1064034

  54. Regmi, J., Poudyal, K.N., Pokhrel, A., Gyawali, M., Tripathee, L., Panday, A., Barinelli, A., Aryal, R. (2020). Investigation of aerosol climatology and long-range transport of aerosols over Pokhara, Nepal. Atmosphere 11, 874. https://doi.org/10.3390/atmos11080874

  55. Robinson, E.S., Gu, P., Ye, Q., Li, H.Z., Shah, R.U., Apte, J.S., Robinson, A.L., Presto, A.A. (2018). Restaurant impacts on outdoor air quality: Elevated organic aerosol mass from restaurant cooking with neighborhood-scale plume extents. Environ. Sci. Technol. 52, 9285–9294. https://doi.org/10.1021/acs.est.8b02654

  56. Ruzer, L.S., Harley, N.H. (2005). Aerosols handbook: measurement, dosimetry, and health effects. CRC press.

  57. Sarkar, C., Sinha, V., Sinha, B., Panday, A.K., Rupakheti, M., Lawrence, M.G. (2017). Source apportionment of NMVOCs in the Kathmandu Valley during the SusKat-ABC international field campaign using positive matrix factorization. Atmos. Chem. Phys. 17, 8129–8156. https://doi.org/10.5194/acp-17-8129-2017

  58. Sayahi, T., Butterfield, A., Kelly, K.E. (2019). Long-term field evaluation of the Plantower PMS low-cost particulate matter sensors. Environ. Pollut. 245, 932–940. https://doi.org/10.1016/j.​envpol.2018.11.065

  59. Schwartz, J., Dockery, D.W., Neas, L.M. (1996). Is daily mortality associated specifically with fine particles? J. Air Waste Manage. Assoc. 46, 927–939. https://doi.org/10.1080/10473289.1996.​10467528

  60. Schwarze, P.E., Øvrevik, J., Låg, M., Refsnes, M., Nafstad, P., Hetland, R.B., Dybing, E. (2006). Particulate matter properties and health effects: Consistency of epidemiological and toxicological studies. Hum. Exp. Toxicol. 25, 559–579. https://doi.org/10.1177/096032706072520

  61. Segura, S., Estellés, V., Utrillas, M.P., Martínez-Lozano, J.A. (2017). Long term analysis of the columnar and surface aerosol relationship at an urban European coastal site. Atmos. Environ. 167, 309–322. https://doi.org/10.1016/j.atmosenv.2017.08.012

  62. Shakya, K.M., Rupakheti, M., Shahi, A., Maskey, R., Pradhan, B., Panday, A., Puppala, S.P., Lawrence, M., Peltier, R.E. (2017). Near-road sampling of PM2.5, BC, and fine-particle chemical components in Kathmandu Valley, Nepal. Atmos. Chem. Phys. 17, 6503–6516. https://doi.org/​10.5194/acp-17-6503-2017

  63. Shiraiwa, M., Ueda, K., Pozzer, A., Lammel, G., Kampf, C.J., Fushimi, A., Enami, S., Arangio, A.M., Fröhlich-Nowoisky, J., Fujitani, Y., Furuyama, A., Lakey, P.S.J., Lelieveld, J., Lucas, K., Morino, Y., Pöschl, U., Takahama, S., Takami, A., Tong, H., Weber, B., et al. (2017). Aerosol health effects from molecular to global scales. Environ. Sci. Technol. 51, 13545–13567. https://doi.org/​10.1021/acs.est.7b04417

  64. Shrestha, S., Puppala, S.P., Adhikary, B., Shrestha, K.L., Panday, A.K. (2017). Influence of semi-volatile aerosols on physical and optical properties of aerosols in the Kathmandu Valley. Atmos. Chem. Phys. Discuss. [preprint], https://doi.org/10.5194/acp-2017-287

  65. Stavroulas, I., Grivas, G., Michalopoulos, P., Liakakou, E., Bougiatioti, A., Kalkavouras, P., Fameli, K., Hatzianastassiou, N., Mihalopoulos, N., Gerasopoulos, E. (2020). Field Evaluation of Low-Cost PM Sensors (Purple Air PA-II) Under Variable Urban Air Quality Conditions, in Greece. Atmosphere 11, 926. https://doi.org/10.3390/atmos11090926

  66. van de Hulst, H.C. (1981). Light Scattering by Small Particles (1981st ed.). Dover Publications, Inc.

  67. van Donkelaar, A., Martin, R.V., Brauer, M., Boys, B.L. (2015). Use of satellite observations for long-term exposure assessment of global concentrations of fine particulate matter. Environ. Health Perspect. 123, 135–143. https://doi.org/10.1289/ehp.1408646

  68. Wang, Y.Q. (2014). MeteoInfo: GIS software for meteorological data visualization and analysis. Meteorol. Appl. 21, 360–368. https://doi.org/10.1002/met.1345

  69. Xu, X., Zhang, C. (2020). Estimation of ground-level PM2.5 concentration using MODIS AOD and corrected regression model over Beijing, China. PLoS One, 15, 3–15. https://doi.org/10.1371/​journal.pone.0240430

  70. Yong, Z. (2016). Plantower PMS5003 Data Manual. 3. 

  71. Zhang, H., Hoff, R.M., Engel-Cox, J.A. (2009). The relation between moderate resolution imaging spectroradiometer (MODIS) aerosol optical depth and PM2.5 over the United States: A geographical comparison by U.S. Environmental Protection Agency regions. J. Air Waste Manage. Assoc. 59, 1358–1369. https://doi.org/10.3155/1047-3289.59.11.1358

  72. Zheng, C., Zhao, C., Zhu, Y., Wang, Y., Shi, X., Wu, X., Chen, T., Wu, F., Qiu, Y. (2017). Analysis of influential factors for the relationship between PM2.5 and AOD in Beijing. Atmos. Chem. Phys. 17, 13473–13489. https://doi.org/10.5194/acp-17-13473-2017

  73. Zhong, S., Qian, Y., Zhao, C., Leung, R., Yang, X.Q. (2015). A case study of urbanization impact on summer precipitation in the Greater Beijing Metropolitan Area: Urban heat island versus aerosol effects. J. Geophys. Res. 120, 10903–10914. https://doi.org/10.1002/2015JD023753


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