Scots pine (Pinus sylvestris L.) is a widespread tolerant forest tree-species; however, its adaptability to environmental change differs among sites with various buffering capacity. In this study, we compared the spatial effects of aridity index (AI) and nitrogen deposition (ND) on biomass density in natural and man-made pine stands of differing soil fertility using geographically weighted multiple lag regression. Soil fertility was defined using soil series as zonal trophic (27.9%), acidic (48.2%), gleyed (15.2%) and as azonal exposed (2.5%), maple (2.4%), ash (0.8%), wet (2.1%) and peat (0.9%) under pine stands in the Czech Republic (Central Europe; 4290.5 km²; 130–1298 m a.s.l.). Annual AI and ND in every pine stand were estimated by intersection between raster and vector from 1 × 1 km grid for years 2000, 2003, 2007 and 2010 of severe non-specific forest damage spread. Biomass density was obtained from a MODIS 250 × 250 m raster using the enhanced vegetation index (EVI) for years 2000–2015, with a decrease in EVI indicating non-specific damage. Environmental change was assessed by comparing predictor values at EVI time t and t+λ. Non-specific damage was registered over 51.9% of total forest area. Less than 8.8% of damaged stands were natural and the rest (91.2%) of damaged stands were man-made. Pure pine stands were more damaged than mixed. The ND effect prevailed up to 2007, while AI dominated later. Temporal increasing ND effect under AI effectiveness led to the most significant pine stand damage in 2008 and 2014. Predictors from 2000 to 2007 afflicted 58.5% of non-specifically damaged stands at R² 0.09–0.76 (median 0.38), but from 2000 to 2010 afflicted 57.1% of the stands at R² 0.16–0.75 (median 0.40). The most damaged stands occurred on acidic sites. Mixed forest and sustainable management on natural sites seem as effective remediation reducing damage by ND.

This study employs the grain size distributions and the concentrations and isotopic compositions of Sr, Nd, and Pb in the precipitation samples collected from the Laohugou Glacier (LHG) in northeastern Tibetan Plateau (TP) during August 2014–2015 to investigate seasonal variability in the insoluble precipitation particle sources. Fine dust particle (0.57–27 μm) depositions dominated in autumn and winter, whereas both fine and coarse dust particle (27–100 μm) depositions were found in spring and summer. Furthermore, the concentrations of Sr, Nd, and Pb also varied seasonally—the highest and lowest Sr and Nd concentrations were recorded in spring and autumn, respectively, whereas the highest and lowest Pb concentrations were recorded in winter and summer, respectively. The Sr and Nd isotopes revealed that the dust in the winter precipitation originated predominately from the Taklimakan Desert and that in spring originated from the Badain Jaran and Qaidam deserts. The precipitation residues in summer were derived from a complex mixture of dust sources from the Gobi and other large deserts in northwest China. Autumn residues were predominately sourced from local soil near the LHG as well as from the Qaidam Basin and the northern TP surface soil. The Taklimakan, long suspected as a major source of long-range transported dust, was an insignificant contributor to the precipitation over LHG during spring, summer, and autumn. Further, the Pb isotopic ratios indicated a primary impact of anthropogenic pollutants for most part of the year (except spring). Meteorological data and the MODIS AOD model are in good agreement with the results from the analyses of the Sr, Nd, and Pb isotopes for the LHG particle source, and further clarify the source regions. Thus, this study thus provides new evidence on the seasonal variability of the sources of the residual particles in remote glaciers in Central Asia.

PM₁ might be more hazardous than PM₂.₅ (particulate matter with an aerodynamic diameter ≤ 1 μm and ≤2.5 μm, respectively). However, studies on PM₁ concentrations and its health effects are limited due to a lack of PM₁ monitoring data.To estimate spatial and temporal variations of PM₁ concentrations in China during 2005–2014 using satellite remote sensing, meteorology, and land use information.Two types of Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 aerosol optical depth (AOD) data, Dark Target (DT) and Deep Blue (DB), were combined. Generalised additive model (GAM) was developed to link ground-monitored PM₁ data with AOD data and other spatial and temporal predictors (e.g., urban cover, forest cover and calendar month). A 10-fold cross-validation was performed to assess the predictive ability.The results of 10-fold cross-validation showed R² and Root Mean Squared Error (RMSE) for monthly prediction were 71% and 13.0 μg/m³, respectively. For seasonal prediction, the R² and RMSE were 77% and 11.4 μg/m³, respectively. The predicted annual mean concentration of PM₁ across China was 26.9 μg/m³. The PM₁ level was highest in winter while lowest in summer. Generally, the PM₁ levels in entire China did not substantially change during the past decade. Regarding local heavy polluted regions, PM₁ levels increased substantially in the South-Western Hebei and Beijing-Tianjin region.GAM with satellite-retrieved AOD, meteorology, and land use information has high predictive ability to estimate ground-level PM₁. Ambient PM₁ reached high levels in China during the past decade. The estimated results can be applied to evaluate the health effects of PM₁.

Particulate matter (PM10) is the key indicator of air quality index in Brunei Darussalam and the principal pollutant for haze related episodes in Southeast Asia. This study examined the temporal and spatial distribution of PM10 base on a long-term monitoring data (2009–2014) in order to identify the emission sources and favorable meteorological conditions for high PM10 concentrations across the country. PM10 concentrations measured at the various locations differ significantly but the general temporal characteristics show clear patterns of seasonal variations across the country with the highest concentrations recorded during the southwest monsoon. The high PM10 values defined in the study were not evenly distributed over the years but occurred mostly within the southwest monsoon months of June to September. Further investigations with bivariate polar concentrations plots and k-means clustering demonstrated the significant influence of Southeast Asian regional biomass fires on the high PM10 concentrations recorded across the country. The results of the polar plots and cluster analyses were further confirmed by the evaluations with Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) backward air masses trajectories analysis and the Moderate Resolution Imaging Spectroradiometer (MODIS) fire records. Among the meteorological variables considered, temperature, rainfall and relative humidity were the most important meteorological variables that influence the concentration throughout the year. High PM10 values are associated with high temperatures and low amounts of rainfall and relative humidity. In addition, wind speed and direction also play significant role in the recorded high PM10 concentrations and were mainly responsible for its seasonality during the study period.

We analysed the variability of equivalent black carbon (BC) and ozone (O3) at the global WMO/GAW station Nepal Climate Observatory-Pyramid (NCO-P, 5079 m a.s.l.) in the southern Himalayas, for evaluating the possible contribution of open vegetation fires to the variability of these short-lived climate forcers/pollutants (SLCF/SLCP) in the Himalayan region.We found that 162 days (9% of the data-set) were characterised by acute pollution events with enhanced BC and O3 in respect to the climatological values. By using satellite observations (MODIS fire products and the USGS Land Use Cover Characterization) and air mass back-trajectories, we deduced that 56% of these events were likely to be affected by emissions from open fires along the Himalayas foothills, the Indian Subcontinent and the Northern Indo-Gangetic Plain.These results suggest that open fire emissions are likely to play an important role in modulating seasonal and inter-annual BC and O3 variability over south Himalayas.

A new global aerosol assimilation system adopting a more complex icosahedral grid configuration is developed. Sensitivity tests for the assimilation system are performed utilizing satellite retrieved aerosol optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS), and the results over Eastern Asia are analyzed. The assimilated results are validated through independent Aerosol Robotic Network (AERONET) observations. Our results reveal that the ensemble and local patch sizes have little effect on the assimilation performance, whereas the ensemble perturbation method has the largest effect. Assimilation leads to significantly positive effect on the simulated AOD field, improving agreement with all of the 12 AERONET sites over the Eastern Asia based on both the correlation coefficient and the root mean square difference (assimilation efficiency). Meanwhile, better agreement of the Ångström Exponent (AE) field is achieved for 8 of the 12 sites due to the assimilation of AOD only.

The Moderate Resolution Imaging Spectroradiometer (MODIS) provides daily global coverage, but the 10 km resolution of its aerosol optical depth (AOD) product is not adequate for studying spatial variability of aerosols in urban areas. Recently, a new Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm was developed for MODIS which provides AOD at 1 km resolution. Using MAIAC data, the relationship between MAIAC AOD and PM2.5 as measured by the EPA ground monitoring stations was investigated at varying spatial scales. Our analysis suggested that the correlation between PM2.5 and AOD decreased significantly as AOD resolution was degraded. This is so despite the intrinsic mismatch between PM2.5 ground level measurements and AOD vertically integrated measurements. Furthermore, the fine resolution results indicated spatial variability in particle concentration at a sub-10 km scale. Finally, this spatial variability of AOD within the urban domain was shown to depend on PM2.5 levels and wind speed.

Agricultural residue burning is one of the major causes of greenhouse gas emissions and aerosols in the Indo-Ganges region. In this study, we characterize the fire intensity, seasonality, variability, fire radiative energy (FRE) and aerosol optical depth (AOD) variations during the agricultural residue burning season using MODIS data. Fire counts exhibited significant bi-modal activity, with peak occurrences during April–May and October–November corresponding to wheat and rice residue burning episodes. The FRE variations coincided with the amount of residues burnt. The mean AOD (2003–2008) was 0.60 with 0.87 (+1σ) and 0.32 (−1σ). The increased AOD during the winter coincided well with the fire counts during rice residue burning season. In contrast, the AOD-fire signal was weak during the summer wheat residue burning and attributed to dust and fossil fuel combustion. Our results highlight the need for ‘full accounting of GHG’s and aerosols’, for addressing the air quality in the study area.

Brown carbon (BrC) has been proposed as an important driving factor in climate change due to its light absorption properties. However, our understanding of BrC’s chemical and optical properties are inadequate, particularly at remote regions. This study conducts a comprehensive investigation of BrC aerosols in summer (Aug. 2013) and winter (Jan. 2014) at Southeast Tibetan Plateau, which is ecologically fragile and sensitive to global warming. The concentrations of methanol-soluble BrC (MeS-BrC) are approximately twice of water-soluble BrC (WS-BrC), demonstrating the environmental importance of water-insoluble BrC are previously underestimated with only WS-BrC considered. The mass absorption efficiency of WS-BrC (0.27–0.86 m² g⁻¹) is lower than those in heavily polluted South Asia, indicating a distinct contrast between the two sides of Himalayas. Fluorescence reveals that the absorption of BrC is mainly attributed to humic-like and protein-like substances, which broaden the current knowledge of BrC’s chromophores. Combining organic tracer, satellite MODIS data and air-mass backward trajectory analysis, this study finds BrC is mainly derived from bioaerosols and secondary formation in summer, while long-range transport of biomass burning emissions in winter. Our study provides new insights into the optical and chemical properties of BrC, which may have implications for environmental effect and sources of organic aerosols.

Polycyclic aromatic hydrocarbons (PAHs) are formed by the incomplete combustion of fossil fuels and forest or biomass burning. PAHs undergo long-range atmospheric transport, as evidenced by in situ observations across the Arctic. However, monitored atmospheric concentrations of PAHs indicate that ambient PAH levels in the Arctic do not follow the declining trend of worldwide anthropogenic PAH emissions since the 2000s, suggesting missing sources of PAHs in the Arctic or other places across the Northern Hemisphere. To trace origins and causes for the increasing trend of PAHs in the Arctic, the present study reconstructed PAH emissions from forest fires in the northern boreal forest derived by combining forest carbon stocks and MODIS burned area. We examined the statistical relationships of forest biomass, MODIS burned area, emission factors, and combustion efficiency with different PAH congeners. These relationships were then employed to construct PAH emission inventories from forest biomass burning. We show that for some PAH congeners, for example, benzo[a]pyrene (BaP)—the forest-fire-induced air emissions are almost one order of magnitude higher than previous emission inventories in the Arctic. A global-scale atmospheric chemistry model, GEOS-Chem, was used to simulate air concentrations of BaP, a representative PAH congener primarily emitted from biomass burning, and to quantify the response of BaP to wildfires in the northern boreal forest. The results showed that BaP emissions from wildfires across the northern boreal forest region played a significant role in the contamination and interannual fluctuations of BaP in Arctic air. A source-tagging technique was applied in tracking the origins of BaP pollution from different northern boreal forest regions. We also show that the response of BaP pollution at different Arctic monitoring sites depends on the intensity of human activities.