Atmospheric nitrogen dioxide (NO2) pollution is a major health and social challenge in South African induced mainly by fossil fuel combustions for power generation, transportation and domestic biomass burning for indoor activities. The pollution level is moderated by various environmental and social factors, yet previous studies made use of limited factors or focussed on only industrialised regions ignoring the contributions in large parts of the country. There is a need to assess how socio-environmenral factors, which inherently exhibit variations across space, influence the pollution levels in South Africa. This study therefore aimed to predict annual tropospheric NO2 column density using socio-environmental variables that are widely proven in the literature as sources and sinks of pollution. The environmental variables used to predict NO2 included remotely sensed Enhanced Vegetation Index (EVI), Land Surface Temperature and Aerosol Optical Depth (AOD) while the social data, which were obtained from national household surveys, included energy sources data, settlement patterns, gender and age statistics aggregated at municipality scale. The prediction was accomplished by applying the Multiscale Geographically Weighted Regression that fine-tunes the spatial scale of each variable when building geographically localised relationships. The model returned an overall R2 of 0.92, indicating good predicting performance and the significance of the socio-environmental variables in estimating NO2 in South Africa. From the environmental variables, AOD had the most influence in increasing NO2 pollution while vegetation represented by EVI had the opposite effect of reducing the pollution level. Among the social variables, household electricity and wood usage had the most significant contributions to pollution. Communal residential arrangements significantly reduced NO2, while informal settlements showed the opposite effect. The female proportion was the most important demographic variable in reducing NO2. Age groups had mixed effects on NO2 pollution, with the mid-age group (20-29) being the most important contributor to NO2 emission. The findings of the current study provide evidence that NO2 pollution is explained by socio-economic variables that vary widely across space. This can be achieved reliably using the MGWR approach that produces strong models suited to each locality.

Tropospheric ozone (O3) is a strong greenhouse gas, particularly in the upper troposphere (UT). Limited observations point to a continuous increase in UT O3 in recent decades, but the attribution of UT O3 changes is complicated by large internal climate variability. We show that the anthropogenic signal (\"fingerprint\") in the patterns of UT O3 increases is distinguishable from the background noise of internal variability. The time-invariant fingerprint of human-caused UT O3 changes is derived from a 16-member initial-condition ensemble performed with a chemistry-climate model (CESM2-WACCM6). The fingerprint is largest between 30°S and 40°N, especially near 30°N. In contrast, the noise pattern in UT O3 is mainly associated with the El Niño-Southern Oscillation (ENSO). The UT O3 fingerprint pattern can be discerned with high confidence within only 13 years of the 2005 start of the OMI/MLS satellite record. Unlike the UT O3 fingerprint, the lower tropospheric (LT) O3 fingerprint varies significantly over time and space in response to large-scale changes in anthropogenic precursor emissions, with the highest signal-to-noise ratios near 40°N in Asia and Europe. Our analysis reveals a significant human effect on Earth\'s atmospheric chemistry in the UT and indicates promise for identifying fingerprints of specific sources of ozone precursors.

Current strategies to hold surface warming below a certain level, e. g., 1.5 or 2°C, advocate limiting total anthropogenic cumulative carbon emissions to ∼0.9 or ∼1.25 Eg C (1018 grams carbon), respectively. These allowable emission budgets are based on a near-linear relationship between cumulative emissions and warming identified in various modeling efforts. The IPCC assesses this near-linear relationship with high confidence in its Summary for Policymakers (§D1.1 and Figure SPM.10). Here we test this proportionality in specially designed simulations with a latest-generation Earth system model (ESM) that includes an interactive carbon cycle with updated terrestrial ecosystem processes, and a suite of CMIP simulations (ZecMIP, ScenarioMIP). We find that atmospheric CO2 concentrations can differ by ∼100 ppmv and surface warming by ∼0.31°C (0.46°C over land) for the same cumulated emissions (≈1.2 Eg C, approximate carbon budget for 2°C target). CO2 concentration and warming per 1 Eg of emitted carbon (Transient Climate Response to Cumulative Carbon Emissions; TCRE) depend not just on total emissions, but also on the timing of emissions, which heretofore have been mainly overlooked. A decomposition of TCRE reveals that oceanic heat uptake is compensating for some, but not all, of the pathway dependence induced by the carbon cycle response. The time dependency clearly arises due to lagged carbon sequestration processes in the oceans and specifically on land, viz., ecological succession, land-cover, and demographic changes, etc., which are still poorly represented in most ESMs. This implies a temporally evolving state of the carbon system, but one which surprisingly apportions carbon into land and ocean sinks in a manner that is independent of the emission pathway. Therefore, even though TCRE differs for different pathways with the same total emissions, it is roughly constant when related to the state of the carbon system, i. e., the amount of carbon stored in surface sinks. While this study does not fundamentally invalidate the established TCRE concept, it does uncover additional uncertainties tied to the carbon system state. Thus, efforts to better understand this state dependency with observations and refined models are needed to accurately project the impact of future emissions.

The internal atmospheric waves are gravity waves and occur in the inner part of the fluid system. In this study, a time-fractional model for internal atmospheric waves is investigated with the Caputo-Fabrizio time-fractional differential operator. The analytical solution of the considered model is retrieved by the Elzaki Adomian decomposition method. The variation in the solution is examined for increasing order of the fractional parameter α through numerical and graphical simulations. The accuracy of the obtained results is established by comparing the obtained solution of considered fractional model with the results available in the literature.

Microplastics (MP) have been recognized as an emerging atmospheric pollutant, yet uncertainties persist in their emissions and concentrations. With a bottom-up approach, we estimate 6-hourly MP fluxes at the ocean-atmosphere interface, using as an input the monthly ocean surface MP concentrations simulated by the global oceanic model (NEMO/PISCES-PLASTIC, Nucleus for European Modeling of the Ocean, Pelagic Interaction Scheme for Carbon and Ecosystem Studies), a size distribution estimate for the MP in the micrometer range, and a sea salt emission scheme. The atmospheric dispersion is then simulated with the Lagrangian model FLEXPART. We identify hotspot sources in the tropical regions and highlight the seasonal variability of emissions, atmospheric concentrations, and deposition fluxes both on land and ocean surfaces. Due to the variability of MP concentration during the year, the MP flux from the sea surface appears to follow a seasonality opposite to that of sea salt aerosol emissions. The comparison with existing observations of MP in the marine atmosphere suggests an underestimation of one to 2 orders of magnitude in our current knowledge of the MP in the oceans\' surface. In addition, we show that the MP in the micrometer range is transported efficiently around the globe and can penetrate and linger in the stratosphere over time scales of months. The interaction of these particles with the chemistry and physics of the atmosphere is still mostly unknown and deserves to be further investigated.

Atmospheric microplastics are important contributors to environmental contamination in aquatic and terrestrial systems and pose potential ecological risks. However, studies on atmospheric microplastics are still limited in urban regions of the Tibetan Plateau, a sentinel region for climate and environmental change under a warming climate. In this study, the occurrence and potential ecological risk of atmospheric microplastics were investigated in samples of suspended atmospheric microplastics collected in Lhasa city during the Tibetan New Year in February 2023. The results show that the average abundance of atmospheric microplastics in Lhasa was 7.15 ± 2.46 MPs m-3. The sizes of the detected microplastics ranged from 20.34 to 297.18 μm, approximately 87% of which were smaller than 100 μm. Fragmented microplastics (95.76%) were the dominant shape, followed by fibres (3.75%) and pellets (0.49%). The primary polymer chemical components identified were polyamide (68.73%) and polystyrene (16.61%). The analysis of meteorological data and the backwards trajectory model indicated the air mass in Lhasa mainly controlled by westwards, and the atmospheric microplastics mainly originated from long-distance atmospheric transport. The potential ecological risk index assessment revealed that the atmospheric microplastic pollution in Lhasa was relatively low. This study provides valuable insights and a scientific foundation for future research on the prevention and control of atmospheric microplastic pollution in Lhasa and other ecologically sensitive cities.

Heavy metal accumulation in agricultural products has become a major concern. Previous studies have focused on the transport of heavy metals from the soil and their accumulation in crops. However, recent studies revealed that wheat leaves, ears, and awns can also transport and accumulate heavy metals. Wheat grains can be influenced by two sources of heavy metals: soil contamination and atmospheric deposition. To comprehend the transport characteristics of heavy metals in soil, atmospheric deposition, and wheat, 37 samples each for wheat rhizosphere soil, wheat roots, stems, leaves, and grains were collected. Fifteen samples of atmospheric dry deposition and atmospheric wet deposition were collected from Linshu County (northern area), China. Based on the test data, the characteristics of heavy metals and their distribution in the study area were analyzed. Migration patterns of heavy metals in crops from different sources were investigated using Pearson correlation and redundancy analysis. Finally, a predictive model for heavy metals in wheat grains was developed using multiple linear regression analysis. Significant disparities in the distribution of heavy metals existed among wheat roots, stems, leaves, and grains. The coefficient of variation of heavy metals in atmospheric deposition was relatively high, indicating discernible spatial patterns influenced by human activities. Notably, a positive correlation was observed between the concentration of heavy metals in wheat grains and atmospheric deposition of Hg, Cd, and Pb. Conversely, Zn and Ni levels in wheat grains were significantly negatively associated with soil Zn, Ni, pH, and OM content. The contribution of heavy metal elements from different sources varied in their impact on the grain\'s heavy metal content. Specifically, atmospheric deposition was the primary source of Hg and Pb in wheat grains, while Cd, Ni, Cu, and Zn were predominantly derived from soil. Using a multiple linear regression model, we could accurately predict Hg, Pb, Cd, Ni, Zn, and As concentrations in crop grains. This model can facilitate quantitative evaluation of ecological risk of heavy metals accumulation in crops in the study area.

Data from the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS) instruments onboard the Landsat 8 and Landsat 9 satellite platforms are subject to contamination by cloud cover, with cirrus contributions being the most difficult to detect and mask. To help address this issue, a cirrus detection channel (Band 9) centered within the 1.375-μm water vapor absorption region was implemented on OLI, with a spatial resolution of 30 m. However, this band has not yet been fully utilized in the Collection 2 Landsat 8/9 Level 2 surface temperature data products that are publicly released by U.S. Geological Survey (USGS). The temperature products are generated with a single-channel algorithm. During the surface temperature retrievals, the effects of absorption of infrared radiation originating from the warmer earth\'s surfaces by ice clouds, typically located in the upper portion of the troposphere and re-emitting at much lower temperatures (approximately 220 K), are not taken into consideration. Through an analysis of sample Level 1 TOA and Level 2 surface data products, we have found that thin cirrus cloud features present in the Level 1 1.375-μm band images are directly propagated down to the Level 2 surface data products. The surface temperature errors resulting from thin cirrus contamination can be 10 K or larger. Previously, we reported an empirical and effective technique for removing thin cirrus scattering effects in OLI images, making use of the correlations between the 1.375-μm band image and images of any other OLI bands located in the 0.4-2.5 μm solar spectral region. In this article, we describe a variation of this technique that can be applied to the thermal bands, using the correlations between the Level 1 1.375-μm band image and the 11-μm BT image for the effective removal of thin cirrus absorption effects. Our results from three data sets acquired over spatially uniform water surfaces and over non-uniform land/water boundary areas suggest that if the cirrus-removed TOA 11-μm band BT images are used for the retrieval of the Level 2 surface temperature (ST) data products, the errors resulting from thin cirrus contaminations in the products can be reduced to about 1 K for spatially diffused cirrus scenes.

Methane is an important greenhouse gas1, but the role of trees in the methane budget remains uncertain2. Although it has been shown that wetland and some upland trees can emit soil-derived methane at the stem base3,4, it has also been suggested that upland trees can serve as a net sink for atmospheric methane5,6. Here we examine in situ woody surface methane exchange of upland tropical, temperate and boreal forest trees. We find that methane uptake on woody surfaces, in particular at and above about 2 m above the forest floor, can dominate the net ecosystem contribution of trees, resulting in a net tree methane sink. Stable carbon isotope measurement of methane in woody surface chamber air and process-level investigations on extracted wood cores are consistent with methanotrophy, suggesting a microbially mediated drawdown of methane on and in tree woody surfaces and tissues. By applying terrestrial laser scanning-derived allometry to quantify global forest tree woody surface area, a preliminary first estimate suggests that trees may contribute 24.6-49.9 Tg of atmospheric methane uptake globally. Our findings indicate that the climate benefits of tropical and temperate forest protection and reforestation may be greater than previously assumed.

The latest Triassic was characterised by protracted biotic extinctions concluding in the End-Triassic Extinction (~ 200 Ma) and a global carbon cycle perturbation. The onset of declining diversity is closely related to reducing conditions that spread globally from upper Sevatian (uppermost Norian) to across the Norian-Rhaetian boundary, likely triggered by unusually high volcanic activity. We correlate significant organic carbon cycle perturbations to an increase of CO2 in the ocean-atmosphere system, likely outgassed by the Angayucham igneous province, the onset of which is indicated by the initiation of a rapid decline in 87Sr/86Sr and 188Os/187Os seawater values. A possible causal mechanism involves elevated CO2 levels causing global warming and accelerating chemical weathering, which increased nutrient discharge to the oceans and greatly increased biological productivity. Higher export production and oxidation of organic matter led to a global O2 decrease in marine water across the Norian/Rhaetian boundary (NRB). Biotic consequences of dysoxia/anoxia include worldwide extinctions in some fossil groups, such as bivalves, ammonoids, conodonts, radiolarians.