Sources and atmospheric dynamics of organic aerosol  in New Delhi, India: insights from receptor modeling

Bhandari, Sahil; Gani, Shahzad; Patel, Kanan; Wang, Dongyu; Soni, Prashant; Arub, Zainab; Habib, Gazala; Apte, Joshua S.; Ruiz, Lea Hildebrandt

doi:10.5194/acp-20-735-2020

Cited by 57 publications

(46 citation statements)

References 64 publications

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“…Detailed documentation, including installation instructions and examples, is available online (Hagan and Kroll, 2019). The framework , called "opcsim", consists of two primary components: the code that models OPSs and implements the Mie theory algorithms (Bohren and Huffman, 1983;Sumlin et al, 2018) and the code to build and evaluate aerosol distributions.…”

Section: Methodsmentioning

confidence: 99%

“…As most low-cost OPCs contain an elliptical refocusing mirror to gather the scattered light across many angles, we compute the integrated light-scattering intensity following a procedure first introduced by Jaenicke and Hanusch (Jaenicke and Hanusch, 1993). Mie theory calculations are implemented using equations by Bohren and Huffman (Bohren and Huffman, 1983). The scattering cross section is calculated as…”

Section: Representing An Aerosol Distributionmentioning

confidence: 99%

“…where λ is the wavelength of incident light, is the viewing angle (which ranges from 1 to 2 ), and i 1 and i 2 are the intensity distribution functions (Bohren and Huffman, 1983). Figure 1 depicts the calibration curve generated for an OPC with the characteristics of the Alphasense OPC-N2 (Table 1) using polystyrene latex spheres (PSLs) of different diameters for calibration.…”

Section: Representing An Aerosol Distributionmentioning

confidence: 99%

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Assessing the accuracy of low-cost optical particle sensors using a physics-based approach

2020

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Abstract. Low-cost sensors for measuring particulate matter (PM) offer the ability to understand human exposure to air pollution at spatiotemporal scales that have previously been impractical. However, such low-cost PM sensors tend to be poorly characterized, and their measurements of mass concentration can be subject to considerable error. Recent studies have investigated how individual factors can contribute to this error, but these studies are largely based on empirical comparisons and generally do not examine the role of multiple factors simultaneously. Here, we present a new physics-based framework and open-source software package (opcsim) for evaluating the ability of low-cost optical particle sensors (optical particle counters and nephelometers) to accurately characterize the size distribution and/or mass loading of aerosol particles. This framework, which uses Mie theory to calculate the response of a given sensor to a given particle population, is used to estimate the fractional error in mass loading for different sensor types given variations in relative humidity, aerosol optical properties, and the underlying particle size distribution. Results indicate that such error, which can be substantial, is dependent on the sensor technology (nephelometer vs. optical particle counter), the specific parameters of the individual sensor, and differences between the aerosol used to calibrate the sensor and the aerosol being measured. We conclude with a summary of likely sources of error for different sensor types, environmental conditions, and particle classes and offer general recommendations for the choice of calibrant under different measurement scenarios.

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Section: Methodsmentioning

confidence: 99%

Section: Representing An Aerosol Distributionmentioning

confidence: 99%

Section: Representing An Aerosol Distributionmentioning

confidence: 99%

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Assessing the accuracy of low-cost optical particle sensors using a physics-based approach

2020

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“…This time a nine-factor solution was chosen (Sect. S3) and was developed partly to provide an analysis product that would be comparable with the earlier Aerosol Chemical Speciation Monitor (ACSM) PMF results for Delhi of Bhandari et al (2020). Additionally, it provides information on the association between the organic factors and the ions that are normally associated with inorganic compounds.…”

Section: Source Apportionmentmentioning

confidence: 99%

Seasonal analysis of submicron aerosol in Old Delhi using high-resolution aerosol mass spectrometry: chemical characterisation, source apportionment and new marker identification

et al. 2021

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Abstract. We present the first real-time composition of submicron particulate matter (PM1) in Old Delhi using high-resolution aerosol mass spectrometry (HR-AMS). Old Delhi is one of the most polluted locations in the world, and PM1 concentrations reached ∼ 750 µg m−3 during the most polluted period, the post-monsoon period, where PM1 increased by 188 % over the pre-monsoon period. Sulfate contributes the largest inorganic PM1 mass fraction during the pre-monsoon (24 %) and monsoon (24 %) periods, with nitrate contributing most during the post-monsoon period (8 %). The organics dominate the mass fraction (54 %–68 %) throughout the three periods, and, using positive matrix factorisation (PMF) to perform source apportionment analysis of organic mass, two burning-related factors were found to contribute the most (35 %) to the post-monsoon increase. The first PMF factor, semi-volatility biomass burning organic aerosol (SVBBOA), shows a high correlation with Earth observation fire counts in surrounding states, which links its origin to crop residue burning. The second is a solid fuel OA (SFOA) factor with links to local open burning due to its high composition of polyaromatic hydrocarbons (PAHs) and novel AMS-measured marker species for polychlorinated dibenzodioxins (PCDDs) and polychlorinated dibenzofurans (PCDFs). Two traffic factors were resolved: one hydrocarbon-like OA (HOA) factor and another nitrogen-rich HOA (NHOA) factor. The N compounds within NHOA were mainly nitrile species which have not previously been identified within AMS measurements. Their PAH composition suggests that NHOA is linked to diesel and HOA to compressed natural gas and petrol. These factors combined make the largest relative contribution to primary PM1 mass during the pre-monsoon and monsoon periods while contributing the second highest in the post-monsoon period. A cooking OA (COA) factor shows strong links to the secondary factor, semi-volatility oxygenated OA (SVOOA). Correlations with co-located volatile organic compound (VOC) measurements and AMS-measured organic nitrogen oxides (OrgNO) suggest SVOOA is formed from aged COA. It is also found that a significant increase in chloride concentrations (522 %) from pre-monsoon to post-monsoon correlates well with SVBBOA and SFOA, suggesting that crop residue burning and open waste burning are responsible. A reduction in traffic emissions would effectively reduce concentrations across most of the year. In order to reduce the post-monsoon peak, sources such as funeral pyres, solid waste burning and crop residue burning should be considered when developing new air quality policy.

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“…With particle loadings mostly below 10's of µg m -3 throughout the United States, this assumption is unlikely to be a large source of absolute error. However, if the same approach were used in highly polluted environments where sub-300 nm aerosol loadings can easily reach hundreds of µg m -3 (Bhandari et al, 2020;Gani et al, 2019), changes in the PSD are likely to lead to large errors (in both an absolute and relative sense) in mass loading measurements. Overall, nephelometers and OPC's with high minimum size cutoffs are prone to substantial uncertainties as the underlying PSD changes, whereas for OPC's with low minimum size cutoffs this effect is relatively minor.…”

Section: Changes In the Particle Size Distribution (Psd)mentioning

confidence: 99%

Assessing the accuracy of low-cost optical particle sensors using a physics-based approach

Hagan¹,

Kroll²

2020

Preprint

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Abstract. Low-cost sensors for measuring particulate matter (PM) offer the ability to understand human exposure to air pollution at spatiotemporal scales that have previously been impractical. However, such low-cost PM sensors tend to be poorly characterized, and their measurements of mass concentration can be subject to considerable error. Recent studies have investigated how individual factors can contribute to this error, but these studies are largely based on empirical comparisons and generally do not examine the role of multiple factors simultaneously. Here, we present a new physics-based framework and open-source software package (opcsim) for evaluating the ability of low-cost optical particle sensors (optical particle counters and nephelometers) to accurately characterize the size distribution and/or mass loading of aerosol particles. This framework, which uses Mie Theory to calculate the response of a given sensor to a given particle population, is used to estimate the relative error in mass loading for different sensor types, given variations in relative humidity, aerosol optical properties, and the underlying particle size distribution. Results indicate that such error, which can be substantial, is dependent on the sensor technology (nephelometer vs. optical particle counter), the specific parameters of the individual sensor, and differences between the aerosol used to calibrate the sensor and the aerosol being measured. We conclude with a summary of likely sources of error for different sensor types, environmental conditions, and particle classes, and offer general recommendations for choice of calibrant under different measurement scenarios.

show abstract

Sources and atmospheric dynamics of organic aerosol in New Delhi, India: insights from receptor modeling

Cited by 57 publications

References 64 publications

Assessing the accuracy of low-cost optical particle sensors using a physics-based approach

Assessing the accuracy of low-cost optical particle sensors using a physics-based approach

Seasonal analysis of submicron aerosol in Old Delhi using high-resolution aerosol mass spectrometry: chemical characterisation, source apportionment and new marker identification

Assessing the accuracy of low-cost optical particle sensors using a physics-based approach

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