Environmental science: atmospheres最新文献

Ambient air pollution has been linked to several health endpoints. The WHO attributes 7 million deaths annually to air pollution with particulate matter (PM_2.5) being the pollutant of critical importance due to its devastating health effects. Air quality monitoring is very limited in sub-Saharan African (SSA) countries and although satellite remote sensing has helped to bridge the huge air quality data gaps, these measurements have not been validated against ground-level measurements in these countries. We therefore evaluated the efficiency of low-cost sensors in estimating PM_2.5 concentrations in an African city through comparison of low-cost sensor data with satellite aerosol optical depth (AOD) data leveraging complex machine learning (ML) methods. Low-cost sensor data were collected from a monitoring network in Accra, Ghana, with AOD measurements extracted from the MODIS MCD19A2v061 dataset and processed using the MAIAC algorithm. Ordinary Least Squares regression, Random Forest, Extra Trees, Boosted Decision Trees and XGBoost were used to establish the relationship between AOD and low-cost sensor PM_2.5 measurements incorporating meteorological data. We observed significant positive relationships for two low-cost sensors deployed in the network (Clarity Node S and Airnote). The R² values were, however, low, ranging from 0.18 to 0.27, with the corrected Airnote data recording the highest R². The ML models which integrated temperature and humidity improved the R² values with the Boosted Decision Tree demonstrating the best predictive capability. Seasonal variability was found to have a strong influence on model performances with the dry season model performing significantly better than the wet season model. Consistent with other studies, AOD explained only a small proportion of ground-level PM_2.5 variations. Evidence from this sensor network in Accra suggests that AOD predicts ground-level PM_2.5 measured with low-cost sensors in a manner similar to conventional air monitoring instrumentation. However, for low-cost sensors to be deemed a good substitute for satellite AOD, data correction with complex algorithms developed in the same research location will be required.

Incorporating the reactions of fifty peroxy radicals (RO₂) with the hydroxyl radical (OH) into the global chemistry transport model, STOCHEM-CRI, affected the composition of the troposphere by changing the global burdens of NO_x (−2.7 Gg, −0.5%), O₃ (−2.3 Tg, −0.7%), CO (−3.2 Tg, −0.8%), HO_x (+2.1 Gg, +7.7%), H₂O₂ (+0.5 Tg, +18.3%), RO₂ (−8.0 Gg, −18.2%), RONO₂ (−19.4 Gg, −4.7%), PAN (−0.1 Tg, −3.4%) HNO₃ (−7.4 Gg, −1.3%) and ROOH (−96.9 Gg, −3.8%). The RO₂ + OH addition reactions have a significant impact on HO₂ mixing ratios in tropical regions with up to a 25% increase, resulting in increasing H₂O₂ mixing ratios by up to 50% over oceans. Globally, a significant amount of organic hydrotrioxides (ROOOH) (86.1 Tg per year) are produced from these reactions with CH₃OOOH (67.5 Tg per year, 78%), isoprene-derived ROOOH (5.5 Tg per year, 6%) and monoterpene-derived ROOOH (4.2 Tg per year, 5%) being the most significant contributors. The tropospheric global burden of CH₃OOOH is found to be 0.48 Gg. The highest mixing ratios of ROOOH, of up to 0.35 ppt, are found primarily in the oceans near the tropical land areas. The RO₂ + OH reactions have a small, but noticeable, contribution to OH reactivity (∼5%) over tropical oceans. Additionally, these reactions have a significant impact on RO₂ reactivity over tropical oceans where losses of the CH₃O₂ radical, isoprene derived peroxy radical (ISOPO₂) and monoterpene derived peroxy radical (MONOTERPO₂) by OH can contribute up to 25%, 15% and 50% to the total RO₂ loss, respectively. The changes in RO₂ reactivity influence the global abundances of organic alcohols (ROH) which are important species due to their crucial impact on air quality. The ROOOH generate secondary organic aerosol (SOA) of up to 0.05 μg m⁻³ which affects the Earth's radiation budget because of enhancing modelled organic aerosol by up to 5% and 2000% on land surfaces and the remote tropical oceans, respectively.

Kampala, the political and economic capital of Uganda and one of the fastest urbanizing cities in sub-Saharan Africa, is experiencing a deteriorating trend in air quality. This decline is driven by emissions from multiple diffuse local sources, including transportation, domestic and outdoor cooking, and industries, as well as by sources outside the city airshed, such as seasonal open fires in the region. PM_2.5 (particulate matter under 2.5 μm size) is the key pollutant of concern in the city with monthly spatial heterogeneity of 60–100 μg m⁻³. Outdoor air pollution is distinctly pronounced in the global south cities and lack the necessary capacity and resources to develop integrated air quality management programs including ambient monitoring, emissions and pollution analysis, source apportionment, and preparation of clean air action plans. This paper presents the first comprehensive integrated assessment of air quality in Kampala to define a multi-level intervention framework, utilizing ground measurements from a hybrid network of stations, global reanalysis fields from GEOS-Chem and CAMS simulations, a high-resolution (∼1 km) multi-pollutant emissions inventory for the designated airshed, WRF-CAMx-based PM_2.5 pollution analysis, and a qualitative review of the institutional and policy environment in Kampala. This collation of information documents baseline data for all known sectors, providing a foundational resource for the development of a clean air action plan. The proposed plan aims for better air quality in the region using a combination of short-, medium-, and long-term emission control measures for all the dominate sources and institutionalize pollution tracking mechanisms (like emissions and pollution monitoring and reporting) for effective management of air pollution.

The phase state of atmospheric particles impacts atmospheric processes like heterogeneous reactions, cloud droplet activation, and ice nucleation, influencing Earth's climate. Factors like chemical composition, temperature, and relative humidity govern particle phase states. The Arctic atmosphere is stratified, with varying particle compositions, but vertical profiles of submicron phase states remain poorly understood due to limited aloft measurements. To address this, particle samples were collected via a tethered balloon system (TBS) at the U.S. Department of Energy Atmospheric Radiation Measurement Program's facility at Oliktok Point, Alaska, on November 19, 2020. Using an environmental scanning electron microscope with a tilted Peltier stage to simulate atmospheric conditions, we probed particle phase states, observing near-spherical, dome-like, and flat shapes upon substrate impact. Particles at an altitude of 300 m contained similar, high fractions of viscous particles (79 ± 9%) compared to ground-level (74 ± 5%). Chemical characterization revealed that carbonaceous-rich and carbonaceous sulfate-rich particles dominate ground-level samples, while 300 m samples included more carbonaceous-rich and carbonaceous-coated dust particles. STXM-NEXAFS further highlighted differences in particle mixing states, with a higher abundance of organic and mixed organic–inorganic particles at both altitudes. Integrating chemical composition and phase state measurements demonstrated that carbonaceous-rich and organic-dominated particles exhibited higher viscosities, while inorganic-rich particles displayed lower viscosities. This finding establishes an association between composition and phase state, offering critical insights into the vertical stratification of Arctic particles.

Stabilised Criegee intermediates (sCIs), which are formed in the atmosphere through the ozonolysis of alkenes, are known precursors of sulphate aerosols (SO₄²⁻(p)). Several previous studies have focused on the kinetics of sCI-related chemistry using both experimental and theoretical methods. Nonetheless, detailed evaluations of how the sCI affects global-scale SO₄²⁻(p) formation using chemical transport models (CTMs) have rarely been conducted. In this study, the impact of sCIs on SO₄²⁻(p) and other particulate matter was estimated using a global CTM by implementing approximately 100 chemical reactions associated with CI chemistry. The results suggest that sCIs contribute maximally less than 0.5% in remote areas, such as Amazon rainforests, Central Africa, and Australia. This value is lower than the previously estimated value, despite certain kinetic parameters related to CI chemistry being provisional due to insufficient data. Future work should focus on obtaining these kinetic parameters through experimental studies or theoretical calculations. The sCI that contributed the most to SO₄²⁻(p) formation was E-methyl glyoxal-1-oxide, which was generated by the ozonolysis of methyl vinyl ketone owing to its low-rate coefficient for the loss reaction of unimolecular decomposition and water vapour. The change in SO₄²⁻(p) enhanced the formation of secondary organic aerosols, whereas the reactions of the sCIs with NO₂ decreased the formation of nitrate radicals. The results of the sensitivity analyses showed that in highly industrialised sites in China and India, OH radicals formed by the unimolecular decomposition of vibrationally excited CIs (vCIs) contributed to SO₄²⁻(p) formation, which maximally accounted for nearly ten times more than that of sCIs, whereas the contribution of vCIs and sCIs to SO₄²⁻(p) formation was estimated to be almost equal in rural and remote sites. The estimated sCI loss by HNO₃ and organic acids was comparable to that of the unimolecular decomposition of sCIs and scavenging by water. This study provides full insight into the impact of gas-phase CI chemistry on a global scale.

Correction for ‘Numerical one-dimensional investigations on a multi-cylinder spark ignition engine using hydrogen/ethanol, hydrogen/methanol and gasoline in dual fuel mode’ by Ufaith Qadiri, Environ. Sci.: Atmos., 2024, 4, 233–242, https://doi.org/10.1039/D3EA00139C.

Atmospheric oxidation largely determines the abundance and lifetime of short-lived climate forcers like methane, ozone and aerosols, as well as the removal of pollutants from the atmosphere. Hydroxyl, nitrate and chlorine radicals (OH, NO₃ and Cl), together with ozone (O₃), are the main atmospheric oxidants. Short-lived halogens (SLH) affect the concentrations of these oxidants, either through direct chemical reactions or indirectly by perturbing their main sources and sinks. However, the effect of SLH on the combined abundance of global oxidants during historical periods remains unquantified and is not accounted for in air quality and climate models. Here, we employ a state-of-the-art chemistry–climate model to comprehensively assess the role of SLH on atmospheric oxidation under both pre-industrial (PI) and present-day (PD) conditions. Our results show a substantial reduction in present-day atmospheric oxidation caused by the SLH-driven combined reduction in the global boundary layer levels of OH (16%), NO₃ (38%) and ozone (26%), which is not compensated by the pronounced increase in Cl (2632%). These global differences in atmospheric oxidants show large spatial heterogeneity due to the variability in SLH emissions and their nonlinear chemical interactions with anthropogenic pollution. Remarkably, we find that the effect of SLH was more pronounced in the pristine PI atmosphere, where a quarter (OH: −25%) and half (NO₃: −49%) of the boundary layer concentration of the main daytime and nighttime atmospheric oxidants, respectively, were controlled by SLH chemistry. The lack of inclusion of the substantial SLH-mediated reduction in global atmospheric oxidation in models may lead to significant errors in calculations of atmospheric oxidation capacity, and the concentrations and trends of short-lived climate forcers and pollutants, both historically and at present.

PM_2.5 (particulate matter with an aerodynamic diameter of less than 2.5 μm) exposure at elevated levels has been associated with adverse health outcomes. However, the high spatiotemporal variability of aerosols poses challenges in monitoring PM_2.5 using ground-based measurement networks. Previously, we developed a new method (referred to as HSRL-CH) to estimate surface PM_2.5 concentration and chemical composition using High Spectral Resolution Lidar (HSRL)-retrieved extinction and derived aerosol types. In this study, we evaluate HSRL-CH performance across the United States using HSRL retrievals from five campaigns: DISCOVER-AQ California, SEAC⁴RS, DISCOVER-AQ Texas, DISCOVER-AQ Colorado, and ACEPOL. We assess the remotely derived PM_2.5 estimates against measurements from the EPA Air Quality System (AQS) and compare HSRL-CH-derived aerosol chemical compositions with AQS-measured compositions. Across all campaigns, HSRL-CH-derived PM_2.5 shows a mean absolute error (MAE) of 10.2 μg m⁻³. The DISCOVER-AQ California campaign had the highest MAE (14.8 μg m⁻³), while other campaigns had MAE ≤ 7.2 μg m⁻³. The lowest MAE occurs when dusty mix type aerosols dominate the retrieved aerosol optical depth, while the highest MAE is associated with smoke type aerosols. Different planetary boundary layer height estimates can lead to a 20% difference in MAE. We anticipate that the HSRL-CH method will provide reliable estimates of PM_2.5 concentration and chemical composition once satellite-based HSRL data acquisition becomes feasible.

Urban street trees can affect air pollutant concentrations by reducing ventilation rates in polluted street canyons (increasing concentrations), or by providing surface area for deposition (decreasing concentrations). This paper examines these effects in Rotterdam, the Netherlands, using mobile measurements of nitrogen dioxide (NO₂), particulate matter (PM), black carbon (BC), and ultrafine particulate matter (UFP). The effect of trees is accounted for in regulatory dispersion models (https://www.cimlk.nl) by the application of an empirically determined tree factor, dependent on the existence and density of the tree canopy, to concentrations due to traffic emissions. Here, we examine the effect of street trees on different pollutants using street-level mobile measurements in a detailed case study (repeated measurements of several neighboring streets) and a larger statistical analysis of measurements across the urban core of Rotterdam. We find that in the summertime, when trees are fully leafed-out, the major short-lived traffic-related pollutants of NO₂ and BC have higher concentrations in streets with higher traffic and greater tree cover, while PM_2.5 has slightly lower concentrations in streets with higher tree factor. UFP shows a less clear, but decreasing trend with tree factor. In low-traffic streets and in wintertime (fewer leaves on trees) measurements confirm the importance of leaves to pollutant trapping by trees, by finding no enhancement of NO₂ and BC with increasing tree cover, rather a slightly decreasing trend in pollutant concentrations with tree factor. Our observations are consistent with the dominant effect of (leafed-out) trees being to trap traffic-emitted pollutants at the surface, but that PM_2.5 in street canyons is more often added by transport from outside the street, which can be attenuated by tree cover. Overall, these measurements emphasize that both traffic-emitted and regional sources are important factors that determine air quality in Rotterdam streets, making the effect of street trees different for different pollutants and different seasons.

Correction for ‘Assessing conditions favoring the survival of African dust-borne microorganisms during long-range transport across the tropical Atlantic’ by Ali Hossein Mardi et al., Environ. Sci.: Atmos., 2025, https://doi.org/10.1039/d4ea00093e.