Atmospheric Environment: X最新文献

Low-cost sensors (LCS) have the potential to provide accurate and reliable measurements of air quality in real-time. This improves our ability to monitor, identify sources of pollution and develop mitigation strategies for effective air quality management. However, recent research on LCS has primarily focused on monitoring, exposure assessment, and calibration. In this study, we investigate the applicability of LCS data collected at ambient sites for characterizing and apportioning aerosol sources. Non-negative matrix factorization (NMF) was applied to the size-resolved data collected across five sites within the Indian Institute of Technology Bombay (IITB) campus in Mumbai using the LCS Alphasense OPC-N2. The sampling was done for 15 days at 5 locations in IITB, and each site only had 3 days of data. NMF resolved two factors for three sites, namely aromas (S2), hostel hub (S3) and central library (S4), while three factors were resolved for two sites, namely construction site (S1) and main gate (S5). Two common sources were determined for all the sites: (i) dust and marine source and (ii) traffic and combustion sources, which agree with the sources identified by studies in the literature. The third factor resolved at sites S1 and S5 is representative of heavy-duty diesel vehicles (HDDVs), which is present for a very short period and is captured because of the capability of high temporal resolution of the LCS. This offers a unique, cost-effective advantage of LCS for capturing episodic activities. The study suggests that in low- and middle-income countries with limited air quality monitoring capabilities, the size-time-resolved PM concentration data obtained from a network of low-cost sensors can estimate the pollution sources. This study provided evidence that despite their inherent limitations, LCS can be useful in attaining interpretable information about pollution sources and recommends extensive use of LCS for source characterization in the future.

This study investigates the impact of meteorological variations on the long-term patterns of PM_2.5 in Delhi from 2007 to 2022 using the AirGAM 2022r1 model. Generalized Additive Modeling was employed to analyze meteorology-adjusted (removing the influence of inter-annual variations in meteorology) and unadjusted trends (trends without considering meteorology) while addressing auto-correlation. PM_2.5 levels showed a modest decline of 14 μg m⁻³ unadjusted and 18 μg m⁻³ meteorology-adjusted over the study period. Meteorological conditions and time factors significantly influenced trends. Temperature, wind speed, wind direction, humidity, boundary layer height, medium-height cloud cover, precipitation, and time variables including day-of-week, day-of-year, and overall time, were used as GAM model inputs. The model accounted for 55% of PM_2.5 variability (adjusted R-squared = 0.55). Day-of-week and medium-height cloud cover were non-significant, while other covariates were significant (p < 0.05), except for precipitation (p < 0.1). Wind speed (F-value: 98) showed the strongest correlation, followed by day-of-year (61), years (41.8), planetary boundary layer height (13.7), and temperature (13). Meteorological parameters exhibited significant long-term trends, except for temperature. Inter-annual meteorological variations minimally affected PM_2.5 trends. The model had a Pearson correlation of 0.72 with observed PM_2.5, underestimating episodic peaks due to long-range transport. Partial dependencies revealed a non-linear PM_2.5 relationship with meteorology. Break-point detection identified two potential breakpoints in PM_2.5 time series. The first, on October 1, 2010, saw a significant increase from 103.4 to 162.6 μg m⁻³, potentially due to long-range transport. Comparing meteorology-adjusted and unadjusted trends can aid policymakers in understanding pollution change causes.

Aerosol size distributions near biomass-burning sources undergo rapid evolution, primarily due to coagulation, which significantly alters the particle number size distribution. Existing long-range aerosol transport and climate prediction models often overlook near-source dynamics involving simultaneous coagulation and dispersion. To bridge this gap, the present study introduces a coagulation-dispersion model and provides semi-analytical solutions for the effective size distribution parameters. The precise solution for a diffusion-less coagulating plume with spatially varying particle concentration supports the conceptual accuracy of the semi-analytical parameterization for dispersion-coagulation model. These solutions form the basis for a parameterization scheme that considers input parameters such as source dimensions, particle mass flux, particle size, and atmospheric conditions. Utilizing this parameterization for case-specific biomass burning emissions shows a decrease in number emission rate by approximately a factor of 600, while the count median diameter of the initial size distribution increases by around 7 times. Additionally, we estimate the optical properties of aerosols both before and after the introduction of the near-source parameterization scheme. Results indicate an increase by a factor of 4 in the aerosol extinction coefficient and by a factor of ∼20 in the scattering coefficient, which will significantly influence the calculation of aerosol optical properties in global models. These changes in optical properties primarily stem from modifications in aerosol size distribution resulting from near-source aerosol dynamics. The results are further discussed.

A global effort towards improved quantitative understanding of greenhouse gas emissions is taking pace. This includes developing source identification, quantification, and apportionment in an attempt to understand global budget and trends, but also developing monitoring systems making emission reduction commitment verifiable. In this context, we demonstrate a novel approach to continuous methane emission monitoring at the spatial scale of an industrial facility. By combining multi-directional measurements of path-integrated methane concentrations with Bayesian state estimation, we show a realistic tomographic gas plume reconstruction, its evolution in time, and the associated estimation of the source map. The method is validated using measurements from controlled methane releases over a domain of area 120×40 m². For the first demonstration, a two dimensional geometry has been used in the gas flow model; nevertheless, sources are located within 3–12 meters, and mass emission rates are estimated within <30% for 80% of the cases.

Vulnerable individuals close to infected people emitting a respiratory cloud containing infectious load can inhale a pathogen dose, experiencing a more severe impact on their health compared to other individuals breathing the mixed air in the same room. In crowded spaces, this issue is crucial. Employing local airflow patterns can reduce the proximity risk of inhalation and subsequent transmission across short distances. This study proposes an experimental and numerical analysis of a novel personal and portable device creating a short-range air barrier to transmitting airborne pathogens in proximity. The portable device adopts V-shaped air blades affecting the trajectory of the particle-laden respiratory cloud emitted by the respiratory system of the infected individual. Experimental results, supported by CFD analysis, indicate that controlling local airflow through the V-shaped jet significantly reduces local particle concentrations by more than 60%, compared to typical scenarios without a local airflow control.

In this study, we evaluate the relative redox activity of various water-soluble organic aerosol (WSOA) sources in Delhi's winter PM_2.5, focusing on their capacity to generate reactive oxygen species (ROS). Using offline-aerosol mass spectrometry (AMS) and positive matrix factorization (PMF), we identified two oxidized factors—more oxidized oxygenated organic aerosol (MO-OOA) and less oxidized oxygenated organic aerosol (LO-OOA)—and three primary factors, namely nitrogen-enriched hydrocarbon-like organic aerosol (NHOA), biomass-burning organic aerosol (BBOA), and solid-fuel combustion organic aerosol (SFC-OA). The ROS-generating capability of PM_2.5 was assessed using a real-time oxidative potential (OP) measurement system based on the dithiothreitol (DTT) assay. We employed multivariate linear regression technique (MLR) to explore the association between the DTT activity of water-soluble PM_2.5 and these identified factors. We found BBOA, SFCOA, and MO-OOA significantly contributed to volume-normalized OP, with intrinsic water-soluble activities of 39 ± 11, 106 ± 31 and 160 ± 43 pmol/min/μg, respectively. MO-OOA, primarily from non-fossil precursors, serves as a proxy for aged biomass burning, which intensifies during winter and significantly influences the DTT activity. Additionally, OP is significantly influenced by WSOA derived from local incomplete solid fuel combustion sources, including coal and wood burning for household cooking and heating, burning of leaves, biodegradable waste, and garbage along the roadside. Interestingly, water-soluble metals (Mn, Cu, and Fe) showed no discernible contribution to the OP. These findings highlight the need for targeted mitigation strategies addressing local combustion processes and unregulated biomass burning to effectively reduce PM health exposure in Delhi.

This study investigates the impact of black carbon (BC) emissions on monsoon rainfall patterns across the Indian subcontinent. The results show that BC exerts localized warming effects, providing valuable insights into the mechanisms influencing the rainfall distribution across the Indian landmass. The study analyzes the vertical profile of mean tropospheric temperature differences between two sets of simulations: (i) with default BC emissions (WBC) and (ii) with BC emissions reduced by 99% (WoBC), conducted for JJAS, 2017, focusing on the region along the eastern coast. The analysis of the tropospheric temperature variations over the eastern coast reveals significant rainfall differences, primarily attributed to the intensification of convective rainfall. The results indicate that WoBC simulation leads to abnormal cooling in the lower troposphere and warming in the mid-upper troposphere, plausibly linked to the release of latent heat from the enhanced convective activity observed over the region. These alterations in the tropospheric temperature profile correspond remarkably well with the changes in the spatial distribution of rainfall over this area, providing valuable insights into the intricate dynamics of the climate system over this region.

Volatile Organic Compounds (VOCs) serve as precursors for tropospheric ozone (O₃) and Secondary Organic Aerosol (SOA) formation. The formation of O₃ and SOA are the indicators of the oxidative capacity specific to a given chemical environment. The current study investigates the oxidative capacity of the relatively less explored tropical rural atmosphere. This study is accomplished by measuring the concentrations of various VOCs and combining them with OH loss rates to estimate the potentials for O₃ and SOA formation (OFP and SOAP, respectively). Continuous diel VOC measurement data from Gadanki (13.5°N, 79.2°E), Peninsular India, encompassing four distinct seasons and comprising over 4000 samples, have been utilized to estimate OFP and SOAP and their variations across different seasons. Additionally, efforts have been made to comprehend the contribution of different VOC sources to O₃ and SOA formation. The results indicate that, 1, 3, 6-trimethyl benzene (20.09 %) among the VOCs and aromatics (44.37%) among the VOC groups exhibit the highest OFP at the observational site. Among seasons, the post-monsoon period exhibits the highest OFP (31.94%). The increased presence of biogenic VOCs, such as ethylene, propylene, and 1-butene during monsoon, likely due to the increased vegetation cover can be attributed for the elevated OFP. Similarly, n-dodecane (43.22%) and the VOC group of alkanes (50.79%) show the highest SOAP. The summer season has the highest SOAP (29.7%), owing to the enhanced concentrations and photochemistry initiated by OH radicals. Within the PMF-modelled sources, biomass-burning VOCs make a substantial contribution to both OFP and SOAP, distinguishing the rural atmosphere from its urban counterpart, where traffic emissions predominantly influence OFP and SOAP.

This study investigates the effects of significant activity changes on air pollutant concentrations across the three European cities Hamburg, Liège, and Marseille and focuses on the effects of COVID-19 lockdown measures as a case study for such significant activity changes. To identify such effects, this study utilizes urban-scale chemistry transport modeling, embedded in regional-scale Chemistry Transport simulations. The outcomes underscore the significance of considering local conditions and emissions sources, as variations between urban and regional simulations demonstrate. Notably, lockdown regulations yield the most substantial impact in Marseille due to its dense road traffic and port area, with Liège following suit, primarily influenced by regional air quality alterations. Conversely, Hamburg exhibits lower mean changes, attributed to its widespread urban structure. Analysis of modeled exceedances of limit values reveals significant reductions, particularly in areas of urban and road land use. These findings contribute valuable insights into the efficacy of significant activity changes, such as lockdown measures, in mitigating air pollution, underlining the importance of tailored strategies for emission reduction in urban environments.