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.

China has performed crop residue burning (CRB) for a long time and has suffered from resultant environmental pollution. High temporal resolution has not been fully discussed in attempts to address the temporal and spatial impact of CRB in China on air quality. Our study used the MOD14A1 product of the MODerate resolution Imaging Spectrometer (MODIS) to extract the daily CRB for China during the period from 2014 to 2016, and the daily aerosol optical depth (AOD) provided by MODIS Collection 6 was obtained to simultaneously reflect the air pollution. First, the study area was classified into five subregions. A temporal analysis was conducted on the daily variation in the number of CRB events and the regional mean value of AOD, the spatial contribution ratio of CRB on aerosol pollution was then calculated, and finally, a temporal and spatial Pearson correlation was calculated to find the spatially varying relationship between CRB and aerosol. The results suggest the following: (1) CRB possesses seasonal characteristics that are associated with the harvest time or sowing time of major crops in the region. (2) The impact of CRB on aerosol was delayed by 1–6 days. (3) High contribution ratios (70%–90%) occurred in northeast China on a large scale; even when the impact of the CRB on aerosol pollution in the Huang-Huai-Hai river basin occurred on a large scale, the value was merely approximately 30%. Relatively low contributions of CRB have been found in other places, whereas the contribution of CRB was severe in some places with high-density populations. (4) Temporal-spatial correlation provided an accurate index to reflect the correlation of CRB and aerosol in a specific location, which suggests that, in places with large scale and dense CRB, CRB tends to have a high positive correlation with aerosol pollution for each day.

The aim of this study is to investigate the variability of aerosol over The Kingdom of Saudi Arabia. For this analysis, Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue (DB) Aerosol Optical Depth (AOD) product from Terra and Aqua satellites for the years 2000–2013 is used. The product is validated using AERONET data from ground stations, which are situated at Solar Village Riyadh and King Abdullah University of Science and Technology (KAUST) Jeddah. The results show that both Terra and Aqua satellites exhibit a tendency to show the spatial variation of AOD with Aqua being better than Terra to represent the ground based AOD measurements over the study region. The results also show that the eastern, central, and southern regions of the country have a high concentration of AOD during the study period. The validation results show the highest correlation coefficient between Aqua and KAUST data with a value of 0.79, whilst the Aqua and Solar Village based AOD indicates the lowest Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values which are, 0.17 and 0.12 respectively. Furthermore, the Relative Mean Bias (RMB) based analysis show that the DB algorithm overestimates the AOD when using Terra and Solar Village data, while it underestimates the AOD when using Aqua with Solar Village and KAUST data. The RMB value for Aqua and Solar Village data indicates that the DB algorithm is close to normal in the study region.