Global Sources of Fine Particulate Matter: Interpretation of PM2.5 Chemical Composition Observed by SPARTAN using a Global Chemical Transport Model.

Exposure to ambient fine particulate matter (PM2.5) is a leading risk factor for the global burden of disease. However, uncertainty remains about PM2.5 sources. We use a global chemical transport model (GEOS-Chem) simulation for 2014, constrained by satellite-based estimates of PM2.5 to interpret globally dispersed PM2.5 mass and composition measurements from the ground-based surface particulate matter network (SPARTAN). Measured site mean PM2.5 composition varies substantially for secondary inorganic aerosols (2.4-19.7 µg/m3), mineral dust (1.9-14.7 µg/m3), residual/organic matter (2.1-40.2 µg/m3), and black carbon (1.0-7.3 µg/m3). Interpretation of these measurements with the GEOS-Chem model yields insight into sources affecting each site. Globally, combustion sectors such as residential energy use (7.9 µg/m3), industry (6.5 µg/m3), and power generation (5.6 µg/m3) are leading sources of outdoor global population-weighted PM2.5 concentrations. Global population-weighted organic mass is driven by the residential energy sector (64%) whereas population-weighted secondary inorganic concentrations arise primarily from industry (33%) and power generation (32%). Simulation-measurement biases for ammonium nitrate and dust identify uncertainty in agricultural and crustal sources. Interpretation of initial PM2.5 mass and composition measurements from SPARTAN with the GEOS-Chem model constrained by satellite-based PM2.5 provides insight into sources and processes that influence the global spatial variation in PM2.5 composition.

Contribution of fossil and biomass-derived secondary organic carbon to winter water-soluble organic aerosols in Delhi, India.

Delhi, among the world's most polluted megacities, is a hotspot of particulate matter emissions, with high contribution from organic aerosol (OA), affecting health and climate in the entire northern India. While the primary organic aerosol (POA) sources can be effectively identified, an incomplete source apportionment of secondary organic aerosol (SOA) causes significant ambiguity in the management of air quality and the assessment of climate change. Present study uses positive matrix factorization analysis on the water-soluble organic aerosol (WSOA) data from the offline-aerosol mass spectrometry (AMS). It revealed POA as the dominant source of WSOA, with biomass-burning OA (31-34 %) and solid fuel combustion OA (∼21 %) being two major contributors. Here we use water-solubility fingerprints to track the SOA precursors, such as oxalates or organic nitrates, instead of identifying them based on their O:C ratio. Non-fossil precursors dominate in more oxidized oxygenated organic carbon (MO-OOC) (∼90 %), a proxy for aged secondary organic carbon (SOC), by coupling offline-AMS with 14C measurements. On the contrary, the oxidation of fossil fuel emissions produces a large quantity of fresh fossil SOC, which accounts for ∼75 % of less oxidized oxygenated organic carbon (LO-OOC). Our study reveals that apart from major POA contributions, large fractions of fossil (10-14 %) and biomass-derived SOA (23-30 %) contribute significantly to the total WSOA load, having impact on climate and air quality of the Delhi megacity. Our study reveals that large-scale unregulated biomass burning was not only found to dominate in POA but was also observed to be a significant contributor to SOA with implications on human health, highlighting the need for effective control strategies.

Elemental Characterization of Ambient Particulate Matter for a Globally Distributed Monitoring Network: Methodology and Implications.

Global ground-level measurements of elements in ambient particulate matter (PM) can provide valuable information to understand the distribution of dust and trace elements, assess health impacts, and investigate emission sources. We use X-ray fluorescence spectroscopy to characterize the elemental composition of PM samples collected from 27 globally distributed sites in the Surface PARTiculate mAtter Network (SPARTAN) over 2019-2023. Consistent protocols are applied to collect all samples and analyze them at one central laboratory, which facilitates comparison across different sites. Multiple quality assurance measures are performed, including applying reference materials that resemble typical PM samples, acceptance testing, and routine quality control. Method detection limits and uncertainties are estimated. Concentrations of dust and trace element oxides (TEO) are determined from the elemental dataset. In addition to sites in arid regions, a moderately high mean dust concentration (6 µg/m3) in PM2.5 is also found in Dhaka (Bangladesh) along with a high average TEO level (6 µg/m3). High carcinogenic risk (>1 cancer case per 100000 adults) from airborne arsenic is observed in Dhaka (Bangladesh), Kanpur (India), and Hanoi (Vietnam). Industries of informal lead-acid battery and e-waste recycling as well as coal-fired brick kilns likely contribute to the elevated trace element concentrations found in Dhaka.

Increased aerosols can reverse Twomey effect in water clouds through radiative pathway.

Aerosols play important roles in modulations of cloud properties and hydrological cycle by decreasing the size of cloud droplets with the increase of aerosols under the condition of fixed liquid water path, which is known as the first aerosol indirect effect or Twomey-effect or microphysical effect. Using high-quality aerosol data from surface observations and statistically decoupling the influence of meteorological factors, we show that highly loaded aerosols can counter this microphysical effect through the radiative effect to result both the decrease and increase of cloud droplet size depending on liquid water path in water clouds. The radiative effect due to increased aerosols reduces the moisture content, but increases the atmospheric stability at higher altitudes, generating conditions favorable for cloud top entrainment and cloud droplet coalescence. Such radiatively driven cloud droplet coalescence process is relatively stronger in thicker clouds to counter relatively weaker microphysical effect, resulting the increase of cloud droplet size with the increase of aerosol loading; and vice-versa in thinner clouds. Overall, the study suggests the prevalence of both negative and positive relationships between cloud droplet size and aerosol loading in highly polluted regions.

Open Waste Canals as Potential Sources of Antimicrobial Resistance Genes in Aerosols in Urban Kanpur, India.

Understanding the movement of antimicrobial resistance genes (ARGs) in the environment is critical to managing their spread. To assess potential ARG transport through the air via urban bioaerosols in cities with poor sanitation, we quantified ARGs and a mobile integron (MI) in ambient air over periods spanning rainy and dry seasons in Kanpur, India (n = 53), where open wastewater canals (OWCs) are prevalent. Gene targets represented major antibiotic groups-tetracyclines (tetA), fluoroquinolines (qnrB), and beta-lactams (blaTEM)-and a class 1 mobile integron (intI1). Over half of air samples located near, and up to 1 km from OWCs with fecal contamination (n = 45) in Kanpur had detectable targets above the experimentally determined limits of detection (LOD): most commonly intI1 and tetA (56% and 51% of samples, respectively), followed by blaTEM (8.9%) and qnrB (0%). ARG and MI densities in these positive air samples ranged from 6.9 × 101 to 5.2 × 103 gene copies/m3 air. Most (7/8) control samples collected 1 km away from OWCs were negative for any targets. In comparing experimental samples with control samples, we found that intI1 and tetA densities in air are significantly higher (P = 0.04 and P = 0.01, respectively, alpha = 0.05) near laboratory-confirmed fecal contaminated waters than at the control site. These data suggest increased densities of ARGs and MIs in bioaerosols in urban environments with inadequate sanitation. In such settings, aerosols may play a role in the spread of AR.

Real-time characterization and source apportionment of fine particulate matter in the Delhi megacity area during late winter.

National Capital Region (NCR) encompassing New Delhi is one of the most polluted urban metropolitan areas in the world. Real-time chemical characterization of fine particulate matter (PM1 and PM2.5) was carried out using three aerosol mass spectrometers, two aethalometers, and one single particle soot photometer (SP2) at two sites in Delhi (urban) and one site located ~40 km downwind of Delhi, during January-March 2018. The campaign mean PM2.5 (NR-PM2.5 + BC) concentrations at the two urban sites were 153.8 ± 109.4 µg.m-3 and 127.8 ± 83.2 µg.m-3, respectively, whereas PM1 (NR-PM1 + BC) was 72.3 ± 44.0 µg.m-3 at the downwind site. PM2.5 particles were composed mostly of organics (43-44)% followed by chloride (14-17)%, ammonium (9-11)%, nitrate (9%), sulfate (8-10)%, and black carbon (11-16)%, whereas PM1 particles were composed of 47% organics, 13% sulfate as well as ammonium, 11% nitrate as well as chloride, and 5% black carbon. Organic aerosol (OA) source apportionment was done using positive matrix factorization (PMF), solved using an advanced multi-linear engine (ME-2) model. Highly mass-resolved OA mass spectra at one urban and downwind site were factorized into three primary organic aerosol (POA) factors including one traffic-related and two solid-fuel combustion (SFC), and three oxidized OA (OOA) factors. Whereas unit mass resolution OA at the other urban site was factorized into two POA factors related to traffic and SFC, and one OOA factor. OOA constituted a majority of the total OA mass (45-55)% with maximum contribution during afternoon hours ~(70-80)%. Significant differences in the absolute OOA concentration between the two urban sites indicated the influence of local emissions on the oxidized OA formation. Similar PM chemical composition, diurnal and temporal variations at the three sites suggest similar type of sources affecting the particulate pollution in Delhi and adjoining cities, but variability in mass concentration suggest more local influence than regional.

Chemical characterization of PM2.5 and source apportionment of organic aerosol in New Delhi, India.

Delhi is one of the most polluted cities worldwide and a comprehensive understanding and deeper insight into the air pollution and its sources is of high importance. We report 5 months of highly time-resolved measurements of non-refractory PM2.5 and black carbon (BC). Additionally, source apportionment based on positive matrix factorization (PMF) of the organic aerosol (OA) fraction is presented. The highest pollution levels are observed during winter in December/January. During that time, also uniquely high chloride concentrations are measured, which are sometimes even the most dominant NR-species in the morning hours. With increasing temperature, the total PM2.5 concentration decreases steadily, whereas the chloride concentrations decrease sharply. The concentrations measured in May are roughly 6 times lower than in December/January. PMF analysis resolves two primary factors, namely hydrocarbon-like (traffic-related) OA (HOA) and solid fuel combustion OA (SFC-OA), and one or two secondary factors depending on the season. The uncertainties of the PMF analysis are assessed by combining the random a-value approach and the bootstrap resampling technique of the PMF input. The uncertainties for the resolved factors range from ±18% to ±19% for HOA, ±7% to ±19% for SFC-OA and ±6 % to ±11% for the OOAs. The average correlation of HOA with equivalent black carbon from traffic (eBCtr) is R2 = 0.40, while SFC-OA has a correlation of R2 = 0.78 with equivalent black carbon from solid fuel combustion (eBCsf). Anthracene (m/z 178) and pyrene (m/z 202) (PAHs) are mostly explained by SFC-OA and follow its diurnal trend (R2 = 0.98 and R2 = 0.97). The secondary oxygenated aerosols are dominant during daytime. The average contribution during the afternoon hours (1 pm-5 pm) is 59% to the total OA mass, with contributions up to 96% in May. In contrast, the primary sources are more important during nighttime: the mean nightly contribution (22 pm-3 am) to the total OA mass is 48%, with contributions up to 88% during some episodes in April.

Development of a light scattering solver applicable to particles of arbitrary shape on the basis of the surface-integral equations method of Müller type. I. Methodology, accuracy of calculation, and electromagnetic current on the particle surface.

We develop a numerical algorithm for calculating the light-scattering properties of small particles of arbitrary shape on the basis of a method involving surface integral equations. The calculation error was estimated by performing a comparison between the proposed method and the exact Mie method with regard to the extinction efficiency factor, and the results show that the error is less than 1% when four or more nodes per wavelength are set on the surface of a spherical particle. The accuracy fluctuates in accordance with the distribution of nodal points on the particle surface with respect to the direction of propagation of the incident light. From our examinations, it is shown that the polar incidence alignment yields higher accuracy than equator incidence when a "latitude-longitude" type of mesh generation is adopted. The electric currents on the particle surface and the phase functions of all scattering directions are shown for particles shaped as spheres or hexagonal columns. It is shown that the phase function for a hexagonal column has four or eight cold spots. The phase function of a randomly oriented hexagonal column shows halolike peaks with size parameters of up to 20. This method can be applied to particles with a size parameter of up to about 20 without using the symmetry characteristic of the particle.

Aerosol-induced intensification of cooling effect of clouds during Indian summer monsoon.

Measurements and models show that enhanced aerosol concentrations can modify macro- and micro-physical properties of clouds. Here, we examine the effect of aerosols on continental mesoscale convective cloud systems during the Indian summer monsoon and find that these aerosol-cloud interactions have a net cooling effect at the surface and the top-of-atmosphere. Long-term (2002-2016) satellite data provide evidence of aerosol-induced cloud invigoration effect (AIvE) during the Indian summer monsoon. The AIvE leads to enhanced formation of thicker stratiform anvil clouds at higher altitudes. These AIvE-induced stratiform anvil clouds are also relatively brighter because of the presence of smaller sized ice particles. As a result, AIvE-induced increase in shortwave cloud radiative forcing is much larger than longwave cloud radiative forcing leading to the intensified net cooling effect of clouds over the Indian summer monsoon region. Such aerosol-induced cooling could subsequently decrease the surface diurnal temperature range and have significant feedbacks on lower tropospheric turbulence in a warmer and polluted future scenario.