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The Atmospheric Pollution and Human Health in a Chinese Megacity (APHH-Beijing) programme is an international collaborative project focusing on understanding the sources, processes and health effects of air pollution in the Beijing megacity. APHH-Beijing brings together leading China and UK research groups, state-of-the-art infrastructure and air quality models to work on four research themes: (1) sources and emissions of air pollutants; (2) atmospheric processes affecting urban air pollution; (3) air pollution exposure and health impacts; and (4) interventions and solutions. Themes 1 and 2 are closely integrated and support Theme 3, while Themes 1–3 provide scientific data for Theme 4 to develop cost-effective air pollution mitigation solutions. This paper provides an introduction to (i) the rationale of the APHH-Beijing programme and (ii) the measurement and modelling activities performed as part of it. In addition, this paper introduces the meteorology and air quality conditions during two joint intensive field campaigns – a core integration activity in APHH-Beijing. The coordinated campaigns provided observations of the atmospheric chemistry and physics at two sites: (i) the Institute of Atmospheric Physics in central Beijing and (ii) Pinggu in rural Beijing during 10 November–10 December 2016 (winter) and 21 May–22 June 2017 (summer). The campaigns were complemented by numerical modelling and automatic air quality and low-cost sensor observations in the Beijing megacity. In summary, the paper provides background information on the APHH-Beijing programme and sets the scene for more focused papers addressing specific aspects, processes and effects of air pollution in Beijing.

This paper evaluates the current status of global modeling of the organic aerosol (OA) in the troposphere and analyzes the differences between models as well as between models and observations. Thirty-one global chemistry transport models (CTMs) and general circulation models (GCMs) have participated in this intercomparison, in the framework of AeroCom phase II. The simulation of OA varies greatly between models in terms of the magnitude of primary emissions, secondary OA (SOA) formation, the number of OA species used (2 to 62), the complexity of OA parameterizations (gas-particle partitioning, chemical aging, multiphase chemistry, aerosol microphysics), and the OA physical, chemical and optical properties. The diversity of the global OA simulation results has increased since earlier AeroCom experiments, mainly due to the increasing complexity of the SOA parameterization in models, and the implementation of new, highly uncertain, OA sources. Diversity of over one order of magnitude exists in the modeled vertical distribution of OA concentrations that deserves a dedicated future study. Furthermore, although the OA / OC ratio depends on OA sources and atmospheric processing, and is important for model evaluation against OA and OC observations, it is resolved only by a few global models. The median global primary OA (POA) source strength is 56 Tg a−1 (range 34–144 Tg a−1) and the median SOA source strength (natural and anthropogenic) is 19 Tg a−1 (range 13–121 Tg a−1). Among the models that take into account the semi-volatile SOA nature, the median source is calculated to be 51 Tg a−1 (range 16–121 Tg a−1), much larger than the median value of the models that calculate SOA in a more simplistic way (19 Tg a−1; range 13–20 Tg a−1, with one model at 37 Tg a−1). The median atmospheric burden of OA is 1.4 Tg (24 models in the range of 0.6–2.0 Tg and 4 between 2.0 and 3.8 Tg), with a median OA lifetime of 5.4 days (range 3.8–9.6 days). In models that reported both OA and sulfate burdens, the median value of the OA/sulfate burden ratio is calculated to be 0.77; 13 models calculate a ratio lower than 1, and 9 models higher than 1. For 26 models that reported OA deposition fluxes, the median wet removal is 70 Tg a−1 (range 28–209 Tg a−1), which is on average 85% of the total OA deposition. Fine aerosol organic carbon (OC) and OA observations from continuous monitoring networks and individual field campaigns have been used for model evaluation. At urban locations, the model–observation comparison indicates missing knowledge on anthropogenic OA sources, both strength and seasonality. The combined model–measurements analysis suggests the existence of increased OA levels during summer due to biogenic SOA formation over large areas of the USA that can be of the same order of magnitude as the POA, even at urban locations, and contribute to the measured urban seasonal pattern. Global models are able to simulate the high secondary character of OA observed in the atmosphere as a result of SOA formation and POA aging, although the amount of OA present in the atmosphere remains largely underestimated, with a mean normalized bias (MNB) equal to −0.62 (−0.51) based on the comparison against OC (OA) urban data of all models at the surface, −0.15 (+0.51) when compared with remote measurements, and −0.30 for marine locations with OC data. The mean temporal correlations across all stations are low when compared with OC (OA) measurements: 0.47 (0.52) for urban stations, 0.39 (0.37) for remote stations, and 0.25 for marine stations with OC data. The combination of high (negative) MNB and higher correlation at urban stations when compared with the low MNB and lower correlation at remote sites suggests that knowledge about the processes that govern aerosol processing, transport and removal, on top of their sources, is important at the remote stations. There is no clear change in model skill with increasing model complexity with regard to OC or OA mass concentration. However, the complexity is needed in models in order to distinguish between anthropogenic and natural OA as needed for climate mitigation, and to calculate the impact of OA on climate accurately.

Abstract In the Scottish uplands, prescribed burning of moorland vegetation is widely practised either to boost gamebird numbers for recreational shooting or to improve livestock grazing. In recent years, this system of land management has become controversial due to concerns over the potential impacts on ecosystem services. However, there are limited data on the extent, distribution or frequency of burning and it is unclear whether there are long‐term trends in burning. Crucially, the extent of burning on peat soils is not well known. We used a time series of Landsat imagery covering 7750 km2 of moorland in Eastern Scotland to detect annual variation in area burnt from 1985 to 2022. Burnt areas were detected using annual changes in Normalised Burn Ratio. An accuracy evaluation conducted over eight sites covering 415 km2 using a combination of Google Earth imagery, and field studies suggested a user's accuracy of 90% and a producer's accuracy of 77%. We estimate an average annual mean area burnt of 61 km2 with large interannual variability and no significant change in area burnt over the 38‐year study period. We estimate that 32% of burning (19 km2 year−1) occurred on deep peat soils with no reduction in burning on deep peat after the revision of national guidelines (the Muirburn Code) in 2017 recommended ceasing this practice. We find that in Eastern Scotland there has been no significant change in moorland area burnt over the last four decades. The fractional area burnt that is on deep peat is a matter of management concern.

Late spring and summer snow cover, the remnants of winter and early spring snowfall, not only possess an intrinsic importance for montane flora and fauna, but also act as a sensitive indicator for climate change. The variability and potential trends in late spring and summer (snowmelt season) snow cover in mountain regions are often poorly documented. May to mid-September Landsat imagery from 1984 to 2022 was used to quantify changes in the snow-covered area of upland regions in the Scottish Highlands. There was substantial annual variability in the area of May to mid-September snow cover combined with a significant decline over the 39-year study period (p = 0.02). Long-term climate data used to show variability in May to mid-September snow cover was positively related to winter snowfall and negatively related to winter and April temperatures. The results from a long-running field survey counting the number of snow patches that survive until the following winter were used to check the veracity of the study. Further, accuracy was estimated through comparison with higher resolution Sentinel-2 imagery, giving a user and producer accuracy rate of 99.8% and 87%, respectively. Projected future warming will further diminish this scarce, valuable habitat, along with its associated plant communities, thus threatening the biodiversity and scenic value of the Scottish Highlands.

Natural forests in Vietnam have experienced rapid declines in the last 70 years, as a result of degradation from logging and conversion of natural forests to timber and rubber plantations. Degradation of natural forests leads to loss of biodiversity and ecosystem services, impacting the livelihoods of surrounding communities. Efforts to address ongoing loss of natural forests, through mechanisms such as Reduced Emissions from Deforestation and Degradation (REDD+), require an understanding of the links between forest degradation and the livelihoods of local communities, which have rarely been studied in Vietnam. We combined information from livelihood surveys, remote sensing and forest inventories around a protected natural forest area in North Central Vietnam. For forest-adjacent communities, we found natural forests contributed an average of 28% of total household income with plantation forests contributing an additional 15%. Although officially prohibited, logging contributed more than half of the total income derived from natural forests. Analysis of Landsat images over the period 1990 to 2014 combined with forest inventory data, demonstrates selective logging was leading to ongoing degradation of natural forests resulting in loss of 3.3 ± 0.8 Mg biomass ha ^−1 yr ^−1 across the protected area. This is equivalent to 1.5% yr ^−1 of total forest biomass, with rates as high as 3% yr ^−1 in degraded and easily accessible parts of the protected area. We estimate that preventing illegal logging would incur local opportunity costs of USD $4.10 ± 0.90 per Mg CO _2 , similar to previous estimates for tropical forest protected areas and substantially less than the opportunity costs in timber or agricultural concessions. Our analysis suggests activities to reduce forest degradation in protected areas are likely to be financially viable through Vietnam’s REDD+ program.

Protected areas (PAs) have been established to conserve tropical forests, but their effectiveness at reducing deforestation is uncertain. To explore this issue, we combined high resolution data of global forest loss over the period 2000-2012 with data on PAs. For each PA we quantified forest loss within the PA, in buffer zones 1, 5, 10 and 15 km outside the PA boundary as well as a 1 km buffer within the PA boundary. We analysed 3376 tropical and subtropical moist forest PAs in 56 countries over 4 continents. We found that 73% of PAs experienced substantial deforestation pressure, with >0.1% a(-1) forest loss in the outer 1 km buffer. Forest loss within PAs was greatest in Asia (0.25% a(-1)) compared to Africa (0.1% a(-1)), the Neotropics (0.1% a(-1)) and Australasia (Australia and Papua New Guinea; 0.03% a(-1)). We defined performance (P) of a PA as the ratio of forest loss in the inner 1 km buffer compared to the loss that would have occurred in the absence of the PA, calculated as the loss in the outer 1 km buffer corrected for any difference in deforestation pressure between the two buffers. To remove the potential bias due to terrain, we analysed a subset of PAs (n = 1804) where slope and elevation in inner and outer 1 km buffers were similar (within 1° and 100 m, respectively). We found 41% of PAs in this subset reduced forest loss in the inner buffer by at least 25% compared to the expected inner buffer forest loss (P

Understanding forest loss patterns in Amazonia, the Earth's largest rainforest region, is critical for effective forest conservation and management. Following the most detailed analysis to date, spanning the entire Amazon and extending over a 14-year period (2001-2014), we reveal significant shifts in deforestation dynamics of Amazonian forests. Firstly, hotspots of Amazonian forest loss are moving away from the southern Brazilian Amazon to Peru and Bolivia. Secondly, while the number of new large forest clearings (>50 ha) has declined significantly over time (46%), the number of new small clearings (<1 ha) increased by 34% between 2001-2007 and 2008-2014. Thirdly, we find that small-scale low-density forest loss expanded markedly in geographical extent during 2008-2014. This shift presents an important and alarming new challenge for forest conservation, despite reductions in overall deforestation rates.

Because forest fires emit substantial NOx and hydrocarbons--known contributors to O3 production--we hypothesize that interannual variation in western U.S. O3 is related to the burned area. To evaluate this hypothesis we used a gridded database of western U.S. summer burned area (BA) and biomass consumed (BC) by fires between 101-125 degrees W. The fire data were compared with daytime summer O3 mixing ratios from nine rural Clean Air Status and Trends Network (CASTNET) and National Park Service (NPS) sites. Large fire years exhibited widespread enhanced O3. The summer BA was significantly correlated with O3 at all sites. For each 1 million acres burned in the western U.S. during summer, we estimate that the daytime mean O3 was enhanced across the region by 2.0 ppbv. For mean and maximum fire years, O3 was enhanced by an average of 3.5 and 8.8 ppbv, respectively. At most sites O3 was significantly correlated with fires in the surrounding 5 x 5 degrees and 10 x 10 degrees regions, but not with fires in the nearest 1 x 1 degree region, reflecting the balance between O3 production and destruction in a high NOx environment. BC was a slightly better predictor of O3, compared with BA. The relationship between O3 and temperature was examined at two sites (Yellowstone and Rocky Mountain National Parks). At these two sites, high fire years were significantly warmer than lowfire years; however, daytime seasonal meantemperature and O3 were not significantly correlated. This indicates that the presence of fire is a more important predictor for O3 than is temperature.

In this study we have evaluated the role of wildfires on concentrations of fine particle (d < 2.5 microm) organic carbon (OC) and particulate mass (PM2.5) in the Western United States for the period 1988-2004. To do this, we examined the relationship between mean summer PM2.5 and OC concentrations at 39 IMPROVE sites with a database of fires developed from federal fire reports. The gridded database of area burned was used to generate a database of biomass fuel burned using ecosystem-specific fuel loads. The OC, PM2.5, and fire data were evaluated for five regions: Northern Rocky Mountains (Region 1), Central Rocky Mountains (Region 2), Southwest (Region 3), California (Region 4), and Pacific Northwest (Region 5). In Regions 1, 2, and 5, we found good correlations of seasonal mean PM2.5 concentrations among the sites within each region. This indicates that a common influence was important in determining the PM concentration at all sites across each region. In Regions 1 and 2, we found a significant correlation between PM2.5 and both the area burned and biomassfuel burned in each region. This relationship is statistically significant using either the area burned or fuel burned, but the correlations are stronger using the biomass fuel burned. In all five regions we found a statistically significant relationship between biomass burned and organic carbon. Using these relationships, we can estimate the amount of PM2.5 due to fires in each region during summer. For the Regions 1 through 5, the average summer-long enhancement of PM2.5 due to fires is 1.84, 1.09, 0.61, 0.81, and 1.21 microg/m3, respectively, and approximately twice these values during large fire years.