Table of contents

Forest and peatland fires in Indonesia emit large quantities of smoke leading to poor air quality across Equatorial Asia. Marlier et al (2015 Environ. Res. Lett.10 085005) explore the contribution of fires occurring on oil palm, timber (wood pulp and paper) and natural forest logging concessions to smoke emissions and exposure of human populations to the resulting air pollution. They find that one third of the population exposure to smoke across Equatorial Asia is caused by fires in oil palm and timber concessions in Sumatra and Kalimantan. Logging concessions have substantially lower fire emissions, and contribute less to air quality degradation. This represents a compelling justification to prevent reclassification of logging concessions into oil palm or timber concessions after logging. This can be achieved by including logged forests in the Indonesian moratorium on new plantations in forested areas.

Screen et al (2015 Environ. Res. Lett.10 084006) find that model simulations forced by past and future Arctic sea-ice conditions provide robust evidence that a variety of extreme weather events both within and beyond the Arctic will be affected by changing sea-ice conditions.

While terrestrial precipitation is a societally highly relevant climate variable, there is little consensus among climate models about its projected 21st century changes. An important source of precipitable water over land is plant transpiration. Plants control transpiration by opening and closing their stomata. The sensitivity of this process to increasing CO₂ concentrations is uncertain. To assess the impact of this uncertainty on future climate, we perform experiments with an intermediate complexity Earth System Climate Model (UVic ESCM) for a range of model-imposed transpiration-sensitivities to CO₂. Changing the sensitivity of transpiration to CO₂ causes simulated terrestrial precipitation to change by −10% to +27% by 2100 under a high emission scenario. This study emphasises the importance of an improved assessment of the dynamics of environmental impact on vegetation to better predict future changes of the terrestrial hydrological and carbon cycles.

Airlines and Air Navigation Service Providers are united in their goal to reduce fuel consumption. While changes to flight operations and technology investments are the focus of a number of studies, our study is among the first to investigate an untapped source of aviation fuel consumption: excess contingency fuel loading. Given the downside risk of fuel exhaustion of diverting to an alternate airport, airline dispatchers may load excess fuel onto an aircraft. Such conservatism comes at a cost of consuming excess fuel, as fuel consumed is a function of, among other factors, aircraft weight. The aim of this paper is to quantify, on a per-flight basis, the fuel burned due to carrying fuel beyond what is needed for foreseeable contingencies, and thereby motivate research, federal guidance, and investments that allow airline dispatchers to reduce fuel uplift while maintaining near zero risks of fuel exhaustion. We merge large publicly available aviation and weather databases with a detailed dataset from a major US airline. Upon estimating factors that capture the quantity fuel consumed due to carrying a pound of weight for a range of aircraft types, we calculate the cost and greenhouse gas emissions from carrying unused fuel on arrival and additional contingency fuel above a conservative buffer for foreseeable contingencies. We establish that the major US carrier does indeed load fuel conservatively. We find that 4.48% of the fuel consumed by an average flight is due to carrying unused fuel and 1.04% of the fuel consumed by an average flight is due to carrying additional contingency fuel above a reasonable buffer. We find that simple changes in flight dispatching that maintain a statistically minimal risk of fuel exhaustion could result in yearly savings of 338 million lbs of CO₂, the equivalent to the fuel consumed from 4760 flights on midsized commercial aircraft. Moreover, policy changes regarding maximum fuel loads or investments that reduce uncertainty or increase the ability to plan flights under uncertainty could yield far greater benefits.

A comprehensive stratosphere-resolving atmospheric model, with interactive stratospheric ozone chemistry, coupled to ocean, sea ice and land components is used to explore the tropospheric and surface impacts of large springtime ozone anomalies in the Arctic stratosphere. Coupling between the Antarctic ozone hole and Southern Hemisphere climate has been identified in numerous studies, but connections of Arctic ozone loss to surface climate have been more difficult to elucidate. Analyzing an ensemble of historical integrations with all known natural and anthropogenic forcings specified over the period 1955–2005, we find that extremely low stratospheric ozone changes are able to produce large and robust anomalies in tropospheric wind, temperature and precipitation in April and May over large portions of the Northern Hemisphere (most notably over the North Atlantic and Eurasia). Further, these ozone-induced surface anomalies are obtained only in the last two decades of the 20th century, when high concentrations of ozone depleting substances generate sufficiently strong stratospheric temperature anomalies to impact the surface climate. Our findings suggest that coupling between chemistry and dynamics is essential for a complete representation of surface climate variability and climate change not only in Antarctica but also in the Arctic.

Interannual variation of Indian summer monsoon rainfall (ISMR) is linked to El Niño-Southern oscillation (ENSO) as well as the Equatorial Indian Ocean oscillation (EQUINOO) with the link with the seasonal value of the ENSO index being stronger than that with the EQUINOO index. We show that the variation of a composite index determined through bivariate analysis, explains 54% of ISMR variance, suggesting a strong dependence of the skill of monsoon prediction on the skill of prediction of ENSO and EQUINOO. We explored the possibility of prediction of the Indian rainfall during the summer monsoon season on the basis of prior values of the indices. We find that such predictions are possible for July–September rainfall on the basis of June indices and for August–September rainfall based on the July indices. This will be a useful input for second and later stage forecasts made after the commencement of the monsoon season.

The area burned by Southern California wildfires has increased in recent decades, with implications for human health, infrastructure, and ecosystem management. Meteorology and fuel structure are universally recognized controllers of wildfire, but their relative importance, and hence the efficacy of abatement and suppression efforts, remains controversial. Southern California's wildfires can be partitioned by meteorology: fires typically occur either during Santa Ana winds (SA fires) in October through April, or warm and dry periods in June through September (non-SA fires). Previous work has not quantitatively distinguished between these fire regimes when assessing economic impacts or climate change influence. Here we separate five decades of fire perimeters into those coinciding with and without SA winds. The two fire types contributed almost equally to burned area, yet SA fires were responsible for 80% of cumulative 1990–2009 economic losses ($3.1 Billion). The damage disparity was driven by fire characteristics: SA fires spread three times faster, occurred closer to urban areas, and burned into areas with greater housing values. Non-SA fires were comparatively more sensitive to age-dependent fuels, often occurred in higher elevation forests, lasted for extended periods, and accounted for 70% of total suppression costs. An improved distinction of fire type has implications for future projections and management. The area burned in non-SA fires is projected to increase 77% (±43%) by the mid-21st century with warmer and drier summers, and the SA area burned is projected to increase 64% (±76%), underscoring the need to evaluate the allocation and effectiveness of suppression investments.

The impacts of unpredictable ecological perturbations are often assessed via measurements of environmental change only after the event has occurred. Temporal series of satellite images provide a cost-effective way to gather information before ecological perturbations occur. However, in previous studies, the disturbances have neither been always centred in time in the series of the focal environmental variable nor has the relevance of the temporal coverage been explicitly tested through factorial designs. In this study, we manipulated the temporal coverage and the position of the disturbance event in the temporal series to examine whether and how the assessment is affected. Specifically, we tested the effect of the Prestige oil spill on monthly sea chlorophyll concentration and net primary productivity along the north-western Spanish coast. We designed planned comparisons through factorial analyses to test two alternative hypotheses: (1) the spill has negative consequences on phytoplankton activity and/or abundance due to physiological constraints or (2) it has positive consequences on phytoplankton abundance as a result of changes in biotic interactions. The relevance of the statistical effects was critically dependent on the temporal coverage and the position of the spill event in the temporal series. Short periods (three years) were insufficient to cover the range of variability even if the disturbance was centred in the time series. Similarly, results from longer time series (up to eight years) in which the event was temporally biased (at the beginning of the time series) also differed from those that were centred in the entire time window. Temporal series for the study of ecological impacts should be as long as necessary to encompass the temporal variability of the study systems (up to nine years in our study case), and the disturbance event should be centred in the time series to reduce potential spurious effects of temporal autocorrelation. However, our results revealed that each one of these requirements alone was not sufficient to encompass all of the natural variability, and thus both requirements should be met. For impact assessments we encourage the use of unbiased satellite data series to complement in situ measurements.

We use state-of-the-art global climate models and observations to show that the projected higher pressures over the British Isles due to global warming are part of an atmospheric response to the decelerating Atlantic meridional overturning circulation (AMOC) causing a reduction in the associated northward heat transport, keeping the North Atlantic relatively cool. However, considerable inter-model differences in the projected weakening of the AMOC lead to a large spread in the projected wind changes. Hence, the uncertainty in the projected reduction of oceanic heat transport is a main source of uncertainty in projections of Western European climate change. Better-constrained projections of European summer climate thus rely heavily on a more realistic representation of ocean processes in climate models.

We examined natural and anthropogenic controls on terrestrial evapotranspiration (ET) changes from 1982 to 2010 using multiple estimates from remote sensing-based datasets and process-oriented land surface models. A significant increasing trend of ET in each hemisphere was consistently revealed by observationally-constrained data and multi-model ensembles that considered historic natural and anthropogenic drivers. The climate impacts were simulated to determine the spatiotemporal variations in ET. Globally, rising CO₂ ranked second in these models after the predominant climatic influences, and yielded decreasing trends in canopy transpiration and ET, especially for tropical forests and high-latitude shrub land. Increasing nitrogen deposition slightly amplified global ET via enhanced plant growth. Land-use-induced ET responses, albeit with substantial uncertainties across the factorial analysis, were minor globally, but pronounced locally, particularly over regions with intensive land-cover changes. Our study highlights the importance of employing multi-stream ET and ET-component estimates to quantify the strengthening anthropogenic fingerprint in the global hydrologic cycle.

Shifting cultivation has traditionally been practiced in the Democratic Republic of Congo by carving agricultural fields out of primary and secondary forest, resulting in the rural complex: a characteristic land cover mosaic of roads, villages, active and fallow fields and secondary forest. Forest clearing has varying impacts depending on where it occurs relative to this area: whether inside it, along its primary forest interface, or in more isolated primary forest areas. The spatial contextualization of forest cover loss is therefore necessary to understand its impacts and plan its management. We characterized forest clearing using spatial models in a Geographical Information System, applying morphological image processing to the Forets d'Afrique Central Evaluee par Teledetection product. This process allowed us to create forest fragmentation maps for 2000, 2005 and 2010, classifying previously homogenous primary forest into separate patch, edge, perforated, fragmented and core forest subtypes. Subsequently we used spatial rules to map the established rural complex separately from isolated forest perforations, tracking the growth of these areas in time. Results confirm that the expansion of the rural complex and forest perforations has high variance throughout the country, with consequent differences in local impacts on forest ecology and habitat fragmentation. Between 2000 and 2010 the rural complex grew by 10.2% (46 182 ha), increasing from 11.9% to 13.1% of the total land area (1.2% change) while perforated forest grew by 74.4% (23 856 ha), from 0.8% to 1.5%. Core forest decreased by 3.8% (54 852 ha), from 38% to 36.6% of the 2010 land area. Of particular concern is the nearly doubling of perforated forest, a land dynamic that represents greater spatial intrusion of forest clearing within core forest areas and a move away from the established rural complex.

Atlantic Multidecadal Variability (AMV) is known for influencing the mid-latitude climate variability, especially over the European region. This letter assesses the impact of the wintertime AMV in a group of 200-year atmospheric-only numerical experiments, in which the atmosphere is forced with positive and negative AMV-like sea surface temperatures (SSTs) and sea ice concentration patterns. Anomalies are applied separately to the whole North Atlantic ocean, to the extratropics (north of 30° N) and to the tropics (between 0° and 30° N). Results show that AMV anomalies considerably affect the North Atlantic Oscillation (NAO), the jet stream variability and the frequency of atmospheric blocking over the Euro-Atlantic sector, resulting in a negative (positive) NAO during positive (negative) AMV. It is found that the bulk of the signal is originated in the tropics and it is associated with a Gill-like response—an anomalous upper tropospheric streamfunction dipole over the tropical Atlantic driven by the SST anomalies—and with the subsequent structural change of the upper-tropospheric jet, which affects the propagation of Rossby waves in the North Atlantic. Conversely, the NAO response is almost negligible when the AMV anomalies are applied only to the extratropics, suggesting that the relevance of SST anomalies along the North Atlantic frontal zone may be overestimated.

The fate of currently frozen permafrost carbon as high-latitude climate warms remains highly uncertain and existing models give widely varying estimates of the permafrost carbon-climate feedback. This uncertainty is due to many factors, including the role that permafrost thaw-induced transitions in soil hydrologic conditions will have on organic matter decomposition rates and the proportion of aerobic to anaerobic respiration. Large-scale permafrost thaw, as predicted by the Community Land Model (CLM) under an unmitigated greenhouse gas emissions scenario, results in significant soil drying due to increased drainage following permafrost thaw, even though permafrost domain water inputs are projected to rise (net precipitation minus evaporation >0). CLM predicts that drier soil conditions will accelerate organic matter decomposition, with concomitant increases in carbon dioxide (CO₂) emissions. Soil drying, however, strongly suppresses growth in methane (CH₄) emissions. Considering the global warming potential (GWP) of CO₂ and CH₄ emissions together, soil drying weakens the CLM projected GWP associated with carbon fluxes from the permafrost zone by more than 50% compared to a non-drying case. This high sensitivity to hydrologic change highlights the need for better understanding and modeling of landscape-scale changes in soil moisture conditions in response to permafrost thaw in order to more accurately assess the potential magnitude of the permafrost carbon-climate feedback.

Enclosed railway stations hosting diesel trains are at risk of reduced air quality as a result of exhaust emissions that may endanger passengers and workers. Air quality measurements were conducted inside London Paddington Station, a semi-enclosed railway station where 70% of trains are powered by diesel engines. Particulate matter (PM_2.5) mass was measured at five station locations. PM size, PM number, oxides of nitrogen (NO_x), and sulphur dioxide (SO₂) were measured at two station locations. Paddington Station's hourly mean PM_2.5 mass concentrations averaged 16 μg m⁻³ [min 2, max 68]. Paddington Station's hourly mean NO₂ concentrations averaged 73 ppb [49, 120] and SO₂ concentrations averaged 25 ppb [15, 37]. While UK train stations are not required to comply with air quality standards, there were five instances where the hourly mean NO₂ concentrations exceeded the EU hourly mean limits (106 ppb) for outdoor air quality. PM_2.5, SO₂, and NO₂ concentrations were compared against Marylebone, a busy London roadside 1.2 km from the station. The comparisons indicated that train station air quality was more polluted than the nearby roadside. PM_2.5 for at least one measurement location within Paddington Station was shown to be statistically higher (P-value <0.05) than Marylebone on 3 out of 4 days. Measured NO₂ within Paddington Station was statistically higher than Marylebone on 3 out of 5 days, while measured SO₂ within Paddington Station was statistically higher than Marylebone on all 3 days.

Artificial removal of CO₂ from the atmosphere (also referred to as negative emissions) has been proposed as a means to restore the climate system to a desirable state, should the impacts of climate change become 'dangerous'. Here we explore whether negative emissions are indeed effective in reversing climate change on human timescales, given the potentially counteracting effect of natural carbon sinks and the inertia of the climate system. We designed a range of CO₂ emission scenarios, which follow a gradual transition to a zero-carbon energy system and entail implementation of various amounts of net-negative emissions at technologically plausible rates. These scenarios are used to force an Earth System Model of intermediate complexity. Results suggest that while it is possible to revert to a desired level of warming (e.g. 2 °C above pre-industrial) after different levels of overshoot, thermosteric sea level rise is not reversible for at least several centuries, even under assumption of large amounts of negative CO₂ emissions. During the net-negative emission phase, artificial CO₂ removal is opposed by CO₂ outgassing from natural carbon sinks, with the efficiency of CO₂ removal—here defined as the drop in atmospheric CO₂ per unit negative emission—decreasing with the total amount of negative emissions.

Human actions, such as converting natural land cover to agricultural or urban land, result in the loss and fragmentation of natural habitat, with important consequences for the provision of ecosystem services. Such habitat loss is especially important for services that are supplied by fragments of natural land cover and that depend on flows of organisms, matter, or people across the landscape to produce benefits, such as pollination, pest regulation, recreation and cultural services. However, our quantitative knowledge about precisely how different patterns of landscape fragmentation might affect the provision of these types of services is limited. We used a simple, spatially explicit model to evaluate the potential impact of natural land cover loss and fragmentation on the provision of hypothetical ecosystem services. Based on current literature, we assumed that fragments of natural land cover provide ecosystem services to the area surrounding them in a distance-dependent manner such that ecosystem service flow depended on proximity to fragments. We modeled seven different patterns of natural land cover loss across landscapes that varied in the overall level of landscape fragmentation. Our model predicts that natural land cover loss will have strong and unimodal effects on ecosystem service provision, with clear thresholds indicating rapid loss of service provision beyond critical levels of natural land cover loss. It also predicts the presence of a tradeoff between maximizing ecosystem service provision and conserving natural land cover, and a mismatch between ecosystem service provision at landscape versus finer spatial scales. Importantly, the pattern of landscape fragmentation mitigated or intensified these tradeoffs and mismatches. Our model suggests that managing patterns of natural land cover loss and fragmentation could help influence the provision of multiple ecosystem services and manage tradeoffs and synergies between services across different human-dominated landscapes.

Determining the time of emergence of climates altered from their natural state by anthropogenic influences can help inform the development of adaptation and mitigation strategies to climate change. Previous studies have examined the time of emergence of climate averages. However, at the global scale, the emergence of changes in extreme events, which have the greatest societal impacts, has not been investigated before. Based on state-of-the-art climate models, we show that temperature extremes generally emerge slightly later from their quasi-natural climate state than seasonal means, due to greater variability in extremes. Nevertheless, according to model evidence, both hot and cold extremes have already emerged across many areas. Remarkably, even precipitation extremes that have very large variability are projected to emerge in the coming decades in Northern Hemisphere winters associated with a wettening trend. Based on our findings we expect local temperature and precipitation extremes to already differ significantly from their previous quasi-natural state at many locations or to do so in the near future. Our findings have implications for climate impacts and detection and attribution studies assessing observed changes in regional climate extremes by showing whether they will likely find a fingerprint of anthropogenic climate change.

As with life cycle assessment (LCA) studies in general, agricultural LCAs often rely on static and outdated inventory data, but literature suggests that agricultural systems may be highly dynamic. Here, we applied life cycle impact assessment methods to investigate the trends and underlying drivers of changes in non-global environmental impacts of major crops in the US. The results show that the impact per hectare corn and cotton generated on the ecological health of freshwater systems decreased by about 50% in the last decade. This change is mainly due to the use of genetically modified (GM) crops, which has reduced the application of insecticides and relatively toxic herbicides such as atrazine. However, the freshwater ecotoxicity impact per hectare soybean production increased by 3-fold, mainly because the spread of an invasive species, soybean aphid, has resulted in an increasing use of insecticides. In comparison, other impact categories remained relatively stable. By evaluating the relative ecotoxicity potential of a large number of pesticides, our analysis offers new insight into the benefits associated with GM crops. Our study also implies that because different impact categories show different degrees of changes, it is worthwhile focusing on the rapidly changing categories when updating agricultural LCA databases under time and resource constraints.

Cryptosporidium is a protozoan parasite that can cause diarrhoea. Human faeces are an important source of Cryptosporidium in surface waters. We present a model to study the impact of sanitation, urbanization and population growth on human emissions of Cryptosporidium to surface waters. We build on a global model by Hofstra et al (2013 Sci. Total Environ.442 10–9) and zoom into Bangladesh and India as illustrative case studies. The model is most sensitive to changes in oocyst excretion and infection rate, and to assumptions on the share of faeces reaching the surface water for different sanitation types. We find urban centres to be hotspots of human Cryptosporidium emissions. We estimate that 53% (Bangladesh) and 91% (India) of total emissions come from urban areas. 50% of oocysts come from only 8% (Bangladesh) and 3% (India) of the country area. In the future, population growth and urbanization may further deteriorate water quality in Bangladesh and India, despite improved sanitation. Under our 'business as usual' ('sanitation improvements') scenario, oocyst emissions will increase by a factor 2.0 (1.2) for India and 2.9 (1.1) for Bangladesh between 2010 and 2050. Population growth, urbanization and sanitation development are important processes to consider for large scale water quality modelling.

We demonstrate how the fundamental timescales of anthropogenic climate change limit the identification of societally relevant aspects of changes in precipitation. We show that it is nevertheless possible to extract, solely from observations, some confident quantified assessments of change at certain thresholds and locations. Maps of such changes, for a variety of hydrologically-relevant, threshold-dependent metrics, are presented. In places in Scotland, for instance, the total precipitation on heavy rainfall days in winter has increased by more than 50%, but only in some locations has this been accompanied by a substantial increase in total seasonal precipitation; an important distinction for water and land management. These results are important for the presentation of scientific data by climate services, as a benchmark requirement for models which are used to provide projections on local scales, and for process-based climate and impacts research to understand local modulation of synoptic and global scale climate. They are a critical foundation for adaptation planning and for the scientific provision of locally relevant information about future climate.

Northern peatlands have been accumulating organic matter since the start of the Holocene, and are now a substantial store of terrestrial carbon. However, their current status as carbon sinks is less clear, because of the possible effects of climate change, air pollution, grazing and drainage etc., and the difficulties of accurate measurement with suitable time resolution. Such measurements are particularly lacking in the UK. Here, we present multi-year eddy covariance measurements of the carbon fluxes at a relatively undisturbed ombrotrophic blanket bog in the Flow Country of northern Scotland. The site consistently acted as a moderate sink for CO₂ over all the measurement years (mean net ecosystem exchange (NEE) of −114 g C m⁻² y⁻¹), similar in magnitude to other measurements in the boreal and tundra zones, and rather higher than the existing measurements at other sites in the UK and Ireland. Generally, the NEE of CO₂ was relatively insensitive to moderate inter-annual variations in weather. Non-CO₂ losses comprised 11% of gross primary production, mainly from methane emissions. Accounting for these terms, the net ecosystem carbon balance was −50 g C-CO₂ eq m⁻² y⁻¹. The contemporary carbon sink was larger than estimates from local peat cores, based on peat accumulation over the last several thousand years, but in the middle of the range of estimates which used spheroidal carbonaceous particles to estimate peat accumulation rates over the last century.

The current climate warming in the Arctic may increase the microbial degradation of vast pools of soil carbon (C); however, the temperature sensitivity of decomposition is often highly dependent on the quality of accumulated soil C. Grazing by reindeer (Rangifer tarandus L.) substantially affects the dominant vegetation and often increases graminoids in relation to dwarf shrubs in ecosystems, but the effect of this vegetation shift on the soil C quality has not been previously investigated. We analyzed the soil C quality and rate of microbially mediated CO₂ release at different temperatures in long-term laboratory incubations using soils from lightly grazed dwarf shrub-dominated and heavily grazed graminoid-dominated tundra ecosystem. The soil C quality was characterized by solid-state cross-polarization magic angle spinning (CPMAS ¹³C NMR) spectroscopy, which showed a higher relative proportion of carbohydrate C under light grazing and higher relative proportion of aliphatic not-O-substituted C under heavy grazing. Initial measurements showed lower temperature sensitivity of the CO₂ release in soils under light grazing compared with soil under heavy grazing, but the overall CO₂ release rate and its temperature sensitivity increased under light grazing as the soil incubation progressed. At the end of incubation, significantly more carbohydrate C had been lost in soils under light grazing compared with heavy grazing. These findings indicate that there may be a link between the grazer-induced effects on soil C quality and the potential of soils to release CO₂ to atmosphere. We suggest that vegetation shifts induced by grazing could influence the proportion of accumulated soil C that is vulnerable to microbial degradation under warming climate.

Livestock farming is the world's largest land use sector and utilizes around 60% of the global biomass harvest. Over the coming decades, climate change will affect the natural resource base of livestock production, especially the productivity of rangeland and feed crops. Based on a comprehensive impact modeling chain, we assess implications of different climate projections for agricultural production costs and land use change and explore the effectiveness of livestock system transitions as an adaptation strategy. Simulated climate impacts on crop yields and rangeland productivity generate adaptation costs amounting to 3% of total agricultural production costs in 2045 (i.e. 145 billion US$). Shifts in livestock production towards mixed crop-livestock systems represent a resource- and cost-efficient adaptation option, reducing agricultural adaptation costs to 0.3% of total production costs and simultaneously abating deforestation by about 76 million ha globally. The relatively positive climate impacts on grass yields compared with crop yields favor grazing systems inter alia in South Asia and North America. Incomplete transitions in production systems already have a strong adaptive and cost reducing effect: a 50% shift to mixed systems lowers agricultural adaptation costs to 0.8%. General responses of production costs to system transitions are robust across different global climate and crop models as well as regarding assumptions on CO₂ fertilization, but simulated values show a large variation. In the face of these uncertainties, public policy support for transforming livestock production systems provides an important lever to improve agricultural resource management and lower adaptation costs, possibly even contributing to emission reduction.

The selection of appropriate species in a reforestation project has always been a complex decision-making problem in which, due mostly to government policies and other stakeholders, not only economic criteria but also other environmental issues interact. Climate change has not usually been taken into account in traditional reforestation decision-making strategies and management procedures. Moreover, there is a lack of agreement on the percentage of each one of the species in reforestation planning, which is usually calculated in a discretionary way. In this context, an effective multicriteria technique has been developed in order to improve the process of selecting species for reforestation in the Mediterranean region of Spain. A hybrid Delphi-AHP methodology is proposed, which includes a consistency analysis in order to reduce random choices. As a result, this technique provides an optimal percentage distribution of the appropriate species to be used in reforestation planning. The highest values of the weight given for each subcriteria corresponded to FR (fire forest response) and PR (pests and diseases risk), because of the increasing importance of the impact of climate change in the forest. However, CB (conservation of biodiversitiy) was in the third position in line with the aim of reforestation. Therefore, the most suitable species were Quercus faginea (19.75%) and Quercus ilex (19.35%), which offer a good balance between all the factors affecting the success and viability of reforestation.

Atmospheric responses to sea ice retreat in the Bering Sea have been linked to recent extreme winters in North America. We investigate the leading factor for the interannual variability of Bering sea ice area (SIA) in early winter (November–December), using canonical correlation analysis based on seasonally resolved atmosphere and ocean data for 1980–2014. We found that the 3-month leading (August–September) geopotential height at 500 hPa (Z500) in the Northern Hemisphere explains 29% of SIA variability. The spatial pattern of Z500 for positive (negative) sea ice anomalies is associated with negative (positive) anomalies over the Gulf of Alaska related to the Pacific transition (PT) pattern. The heat budget analysis indicates that summertime atmospheric conditions influence SIA through the ocean temperature anomalies of the Alaskan Coastal Current forced by atmospheric turbulent heat fluxes. The PT pattern highly correlates with convective precipitation in the western subtropical Pacific, implying that weakened subtropical forcing is the likely cause for the recent extreme winters in North America. Our results present a major factor for interannual variability in the Bering SIA, and further would contribute to the improvement of forecasting winter climate in North America.

As climate change increases the frequency and intensity of extreme heat, cities and their urban heat island (UHI) effects are growing, as are the urban populations encountering them. These mutually reinforcing trends present a growing risk for urban populations. However, we have limited understanding of urban climates during extreme temperature episodes, when additional heat from the UHI may be most consequential. We observed a historically hot summer and historically cold winter using an array of up to 150 temperature and relative humidity sensors in and around Madison, Wisconsin, an urban area of population 402 000 surrounded by lakes and a rural landscape of agriculture, forests, wetlands, and grasslands. In the summer of 2012 (third hottest since 1869), Madison's urban areas experienced up to twice as many hours ⩾32.2 °C (90 °F), mean July T_MAX up to 1.8 °C higher, and mean July T_MIN up to 5.3 °C higher than rural areas. During a record setting heat wave, dense urban areas spent over four consecutive nights above the National Weather Service nighttime heat stress threshold of 26.7 °C (80 °F), while rural areas fell below 26.7 °C nearly every night. In the winter of 2013–14 (coldest in 35 years), Madison's most densely built urban areas experienced up to 40% fewer hours ⩽−17.8 °C (0 °F), mean January T_MAX up to 1 °C higher, and mean January T_MIN up to 3 °C higher than rural areas. Spatially, the UHI tended to be most intense in areas with higher population densities. Temporally, both daytime and nighttime UHIs tended to be slightly more intense during more-extreme heat days compared to average summer days. These results help us understand the climates for which cities must prepare in a warming, urbanizing world.

The existence of anthropogenic climate change remains a public controversy despite the consensus among climate scientists. The controversy may be fed by the existence of scientists from other disciplines publicly casting doubt on the validity of climate science. The extent to which non-climate scientists are skeptical of climate science has not been studied via direct survey. Here we report on a survey of biophysical scientists across disciplines at universities in the Big 10 Conference. Most respondents (93.6%) believe that mean temperatures have risen and most (91.9%) believe in an anthropogenic contribution to rising temperatures. Respondents strongly believe that climate science is credible (mean credibility score 6.67/7). Those who disagree about climate change disagree over basic facts (e.g., the effects of CO₂ on climate) and have different cultural and political values. These results suggest that scientists who are climate change skeptics are outliers and that the majority of scientists surveyed believe in anthropogenic climate change and that climate science is credible and mature.

International climate mitigation efforts are focused on limiting increase in global mean temperature, which has been shown to be proportional to cumulative CO₂ emissions. However, the ability of natural and human systems to successfully adapt to climatic changes depends on both the magnitude and rate of change, the latter of which will depend on how quickly a given level of cumulative emissions occurs. We show that cumulative CO₂ emissions of 4620 Gt CO₂ (reached in 2100 in RCP4.5 and 2057 in RCP8.5) produce globally averaged warming rates that are nearly twice as fast in RCP8.5 than RCP4.5 (0.34 ± 0.08 °C per decade versus 0.19 ± 0.05 °C per decade, respectively). Similarly, the globally averaged velocity of climate change calculated according to the 'nearest equivalent climate' is greater by a factor of ∼2 in RCP8.5 than in RCP4.5 (2.51 ± 0.67 km yr⁻¹ versus 1.32 ± 0.39 km yr⁻¹, respectively), despite equivalent cumulative emissions. These differences in the projected velocity of climate change represent uncertainty for ecosystems that may be unable to adapt to the faster changes. Particularly at risk are boreal forests, of which 48% are projected to experience rates of change beyond their expected adaptive capacity (i.e. >0.3 °C per decade) in RCP4.5, compared with 95% in RCP8.5. Thus, the same budget of cumulative carbon emissions may result in critically different impacts on natural and human systems, depending on the amount of time over which that budget is expended.

In the recent past, the emerging India economy is highly dependent on conventional as well as renewable energy to deal with energy security. Keeping the potential of biomass and its plentiful availability, the Indian government has been encouraging various industrial sectors to generate their own energy from it. The Indian sugar industry has adopted and made impressive growth in bagasse (a renewable biomass, i.e. left after sugercane is crushed) based cogeneration power to fulfil their energy need, as well as to export a big chunk of energy to grid power. Like fossil fuel, bagasse combustion also generates various critical pollutants. This article provides the first ever estimation, current status and overview of magnitude of air pollutant emissions from rapidly growing bagasse based cogeneration technology in Indian sugar mills. The estimated emission from the world's second largest sugar industry in India for particulate matter, NO_X, SO₂, CO and CO₂ is estimated to be 444 ± 225 Gg yr⁻¹, 188 ± 95 Gg yr⁻¹, 43 ± 22 Gg yr⁻¹, 463 ± 240 Gg yr⁻¹ and 47.4 ± 9 Tg yr⁻¹, respectively in 2014. The studies also analyze and identify potential hot spot regions across the country and explore the possible further potential growth for this sector. This first ever estimation not only improves the existing national emission inventory, but is also useful in chemical transport modeling studies, as well as for policy makers.

Role of changing climatic conditions on permafrost degradation and hydrology was investigated in the transition zone between the tundra and forest ecotones at the boundary of continuous and discontinuous permafrost of the lower Yenisei River. Three watersheds of various sizes were chosen to represent the characteristics of the regional landscape conditions. Samples of river flow, precipitation, snow cover, and permafrost ground ice were collected over the watersheds to determine isotopic composition of potential sources of water in a river flow over a two year period. Increases in air temperature over the last forty years have resulted in permafrost degradation and a decrease in the seasonal frost which is evident from soil temperature measurements, permafrost and active-layer monitoring, and analysis of satellite imagery. The lowering of the permafrost table has led to an increased storage capacity of permafrost affected soils and a higher contribution of ground water to river discharge during winter months. A progressive decrease in the thickness of the layer of seasonal freezing allows more water storage and pathways for water during the winter low period making winter discharge dependent on the timing and amount of late summer precipitation. There is a substantial seasonal variability of stable isotopic composition of river flow. Spring flooding corresponds to the isotopic composition of snow cover prior to the snowmelt. Isotopic composition of river flow during the summer period follows the variability of precipitation in smaller creeks, while the water flow of larger watersheds is influenced by the secondary evaporation of water temporarily stored in thermokarst lakes and bogs. Late summer precipitation determines the isotopic composition of texture ice within the active layer in tundra landscapes and the seasonal freezing layer in forested landscapes as well as the composition of the water flow during winter months.

Increasing atmospheric carbon dioxide levels, higher temperatures, altered precipitation patterns, and other climate change impacts have already begun to affect US agriculture and forestry, with impacts expected to become more substantial in the future. There have been numerous studies of climate change impacts on agriculture or forestry, but relatively little research examining the long-term net impacts of a stabilization scenario relative to a case with unabated climate change. We provide an analysis of the potential benefits of global climate change mitigation for US agriculture and forestry through 2100, accounting for landowner decisions regarding land use, crop mix, and management practices. The analytic approach involves a combination of climate models, a crop process model (EPIC), a dynamic vegetation model used for forests (MC1), and an economic model of the US forestry and agricultural sector (FASOM-GHG). We find substantial impacts on productivity, commodity markets, and consumer and producer welfare for the stabilization scenario relative to unabated climate change, though the magnitude and direction of impacts vary across regions and commodities. Although there is variability in welfare impacts across climate simulations, we find positive net benefits from stabilization in all cases, with cumulative impacts ranging from $32.7 billion to $54.5 billion over the period 2015–2100. Our estimates contribute to the literature on potential benefits of GHG mitigation and can help inform policy decisions weighing alternative mitigation and adaptation actions.

Long-term climate experiments up to the year 2300 have been conducted using two full-scale complex Earth system models (ESMs), CESM1(BGC) and MIROC-ESM, for a CO₂ emissions reduction pathway, termed Z650, where annual CO₂ emissions peak at 11 PgC in 2020, decline by 50% every 30 years, and reach zero in 2160. The results have been examined by focusing on the approximate linear relationship between the temperature increase and cumulative CO₂ emissions. Although the temperature increase is nearly proportional to the cumulative CO₂ emissions in both models, this relationship does not necessarily provide a robust basis for the restriction of CO₂ emissions because it is substantially modulated by non-CO₂ forcing. CO₂-induced warming, estimated from the atmospheric CO₂ concentrations in the models, indicates an approximate compensation of nonlinear changes between fast-mode responses to concentration changes at less than 10 years and slow-mode response at more than 100 years due to the thermal inertia of the ocean. In this estimate, CESM1(BGC) closely approximates a linear trend of 1.7 °C per 1000 PgC, whereas MIROC-ESM shows a deviation toward higher temperatures after the emissions peak, from 1.8 °C to 2.4 °C per 1000 PgC over the range of 400–850 PgC cumulative emissions corresponding to years 2000–2050. The evolution of temperature under zero emissions, 2160–2300, shows a slight decrease of about 0.1 °C per century in CESM1(BGC), but remains almost constant in MIROC-ESM. The fast-mode response toward the equilibrium state decreases with a decrease in the airborne fraction owing to continued CO₂ uptake (carbon cycle inertia), whereas the slow-mode response results in more warming owing to continued heat uptake (thermal inertia). Several specific differences are noted between the two models regarding the degree of this compensation and in some key regional aspects associated with sustained warming and long-term climate risks. Overall, elevated temperatures continue for at least a few hundred years under zero emissions.

Development and climate goals together constrain the carbon intensity of production. Using a simple and transparent model that represents committed CO₂ emissions (future emissions expected to come from existing capital), we explore the carbon intensity of production related to new capital required for different temperature targets across several thousand scenarios. Future pathways consistent with the 2 °C target which allow for continued gross domestic product growth require early action to reduce carbon intensity of new production, and either (i) a short lifetime of energy and industry capital (e.g. early retrofit of coal power plants), or (ii) large negative emissions after 2050 (i.e. rapid development and dissemination of carbon capture and sequestration). To achieve the 2 °C target, half of the scenarios indicate a carbon intensity of new production between 33 and 73 g CO₂/$—much lower than the global average today, at 360 g CO₂/$. The average lifespan of energy capital (especially power plants), and industry capital, are critical because they commit emissions far into the future and reduce the budget for new capital emissions. Each year of lifetime added to existing, carbon intensive capital, decreases the carbon intensity of new production required to meet a 2 °C carbon budget by 1.0–1.5 g CO₂/$, and each year of delaying the start of mitigation decreases the required CO₂ intensity of new production by 20–50 g CO₂/$. Constraints on the carbon intensity of new production under a 3 °C target are considerably relaxed relative to the 2 °C target, but remain daunting in comparison to the carbon intensity of the global economy today.

Europe's and the World's northernmost agriculture is very vulnerable to harsh overwintering conditions. It is important from both an economic and societal standpoint to have accurate methods of predicting the severity and impact of the current snow season. Technology has advanced to enable such measurements to be regularly recorded but despite this, a detailed assessment, involving remote sensing , of the impacts of various types of snow season on agricultural yields in northernmost Europe has not previously been undertaken. Here we characterize variation in snow types and concomitant soil frost and ground-ice accumulation at a Norwegian sub-Arctic, maritime-buffered site (Tromsø, Troms County, 69 °N) during the period 1989/90 to 2013/14 and analyse how winter conditions affect agricultural productivity (both measured in the field and using remote sensing). These data were then used to build important predictive modelling approaches. In total, five contrasting types of snow season were identified, from snow-rich with no soil frost and no ground-ice to low snow and considerable soil frost and ground-ice. Conditions of low snow and low soil frost and ground-ice that result from numerous warming events were rare within the time period studied but are predicted to become the dominant snow season type. Agricultural productivity was lowest and claim settlements paid to farmers were highest after winters with high accumulation of plant-damaging, hermetic ground-ice. Deep soil frost per se did not affect primary productivity. Overall, our results together with information from other sources, suggest that icy, low snow conditions are the most challenging of all seasonal types for both the environment and livelihoods in sub-Arctic Norway. Winters with extremely deep snow also cause considerable problems. As winters are expected to warm more than summers, it is likely that the winter climate will become an even stronger regulator of northern primary productivity. To better understand the physical and biological effects of the changing winter climate, there is a requirement for continued and increasing monitoring of winter processes, especially related to frost and ice in the rhizosphere, as this is currently not well covered in national monitoring programs. Continued monitoring will enable further refinement of predictions and will support the better community planning for greatest agricultural benefit.

Environmental justice (EJ) research has relied on ecological analyses of socio-demographic data from areal units to determine if particular populations are disproportionately burdened by toxic risks. This article advances quantitative EJ research by (a) examining whether statistical associations found for geographic units translate to relationships at the household level; (b) testing alternative explanations for distributional injustices never before investigated; and (c) applying a novel statistical technique appropriate for geographically-clustered data. Our study makes these advances by using generalized estimating equations to examine distributive environmental inequities in the Miami (Florida) metropolitan area, based on primary household-level survey data and census block-level cancer risk estimates of hazardous air pollutant (HAP) exposure from on-road mobile emission sources. In addition to modeling determinants of on-road HAP cancer risk among all survey participants, two subgroup models are estimated to examine whether determinants of risk differ based on disadvantaged minority (Hispanic and non-Hispanic Black) versus non-Hispanic white racial/ethnic status. Results reveal multiple determinants of risk exposure disparities. In the model including all survey participants, renter-occupancy, Hispanic and non-Hispanic black race/ethnicity, the desire to live close to work/urban services or public transportation, and higher risk perception are associated with greater on-road HAP cancer risk; the desire to live in an amenity-rich environment is associated with less risk. Divergent subgroup model results shed light on the previously unexamined role of racial/ethnic status in shaping determinants of risk exposures. While lower socioeconomic status and higher risk perception predict significantly greater on-road HAP cancer risk among disadvantaged minorities, the desire to live near work/urban services or public transport predict significantly greater risk among non-Hispanic whites. Findings have important implications for EJ research and practice in Miami and elsewhere.

Estimates of the seasonal and interannual exchanges of carbon dioxide (CO₂) and methane (CH₄) between land ecosystems north of 45°N and the atmosphere are poorly constrained, in part, because of uncertainty in the temporal variability of water-inundated land area. Here we apply a process-based biogeochemistry model to evaluate how interannual changes in wetland inundation extent might have influenced the overall carbon dynamics of the region during the time period 1993–2004. We find that consideration by our model of these interannual variations between 1993 and 2004, on average, results in regional estimates of net methane sources of 67.8 ± 6.2 Tg CH₄ yr⁻¹, which is intermediate to model estimates that use two static inundation extent datasets (51.3 ± 2.6 and 73.0 ± 3.6 Tg CH₄ yr⁻¹). In contrast, consideration of interannual changes of wetland inundation extent result in regional estimates of the net CO₂ sink of −1.28 ± 0.03 Pg C yr⁻¹ with a persistent wetland carbon sink from −0.38 to −0.41 Pg C yr⁻¹ and a upland sink from −0.82 to −0.98 Pg C yr⁻¹. Taken together, despite the large methane emissions from wetlands, the region is a consistent greenhouse gas sink per global warming potential (GWP) calculations irrespective of the type of wetland datasets being used. However, the use of satellite-detected wetland inundation extent estimates a smaller regional GWP sink than that estimated using static wetland datasets. Our sensitivity analysis indicates that if wetland inundation extent increases or decreases by 10% in each wetland grid cell, the regional source of methane increases 13% or decreases 12%, respectively. In contrast, the regional CO₂ sink responds with only 7–9% changes to the changes in wetland inundation extent. Seasonally, the inundated area changes result in higher summer CH₄ emissions, but lower summer CO₂ sinks, leading to lower summer negative greenhouse gas forcing. Our analysis further indicates that wetlands play a disproportionally important role in affecting regional greenhouse gas budgets given that they only occupy approximately 10% of the total land area in the region.

Recent environmental justice (EJ) research has emphasized the need to analyze social inequities in the distribution of natural hazards such as hurricanes and floods, and examine intra-ethnic diversity in patterns of EJ. This study contributes to the emerging EJ scholarship on exposure to flooding and ethnic heterogeneity by analyzing the racial/ethnic and socioeconomic characteristics of the population residing within coastal and inland flood risk zones in the Miami Metropolitan Statistical Area (MSA), Florida—one of the most ethnically diverse MSAs in the U.S. and one of the most hurricane-prone areas in the world. We examine coastal and inland flood zones separately because of differences in amenities such as water views and beach access. Instead of treating the Hispanic population as a homogenous group, we disaggregate the Hispanic category into relevant country-of-origin subgroups. Inequities in flood risk exposure are statistically analyzed using socio-demographic variables derived from the 2010 U.S. Census and 2007–2011 American Community Survey estimates, and 100-year flood risk zones from the Federal Emergency Management Agency (FEMA). Social vulnerability is represented with two neighborhood deprivation indices called economic insecurity and instability. We also analyze the presence of seasonal/vacation homes and proximity to public beach access sites as water-related amenity variables. Logistic regression modeling is utilized to estimate the odds of neighborhood-level exposure to coastal and inland 100-year flood risks. Results indicate that neighborhoods with greater percentages of non-Hispanic Blacks, Hispanics, and Hispanic subgroups of Colombians and Puerto Ricans are exposed to inland flood risks in areas without water-related amenities, while Mexicans are inequitably exposed to coastal flood risks. Our findings demonstrate the importance of treating coastal and inland flood risks separately while controlling for water-related amenities, and recognizing intra-ethnic diversity within the Hispanic category to obtain a more comprehensive assessment of the social distribution of flood risks.

A lack of basic information on optimal nitrogen (N) management often results in over- or under-application of N fertilizer in small-scale intensive rice farming. Here, we present a new database of N input from a survey of 6611 small-scale rice farmers and rice yield in response to added N in 1177 experimental on-farm tests across eight agroecological subregions of China. This database enables us to evaluate N management by farmers and develop an optimal approach to regional N management. We also investigated grain yield, N application rate, and estimated greenhouse gas (GHG) emissions in comparison to N application and farming practices. Across all farmers, the average N application rate, weighted by the area of rice production in each subregion, was 210 kg ha⁻¹ and ranged from 30 to 744 kg ha⁻¹ across fields and from 131 to 316 kg ha⁻¹ across regions. The regionally optimal N rate (RONR) determined from the experiments averaged 167 kg ha⁻¹ and varied from 114 to 224 kg N ha⁻¹ for the different regions. If these RONR were widely adopted in China, approximately 56% of farms would reduce their use of N fertilizer, and approximately 33% would increase their use of N fertilizer. As a result, grain yield would increase by 7.4% from 7.14 to 7.67 Mg ha⁻¹, and the estimated GHG emissions would be reduced by 11.1% from 1390 to 1236 kg carbon dioxide (CO₂) eq Mg⁻¹ grain. These results suggest that to achieve the goals of improvement in regional yield and sustainable environmental development, regional N use should be optimized among N-poor and N-rich farms and regions in China.

Research in the Global North (e.g., US, Europe) has revealed robust patterns of environmental injustice whereby low income and minority residents face exposure to industrial hazards in their neighborhoods. A small body of research suggests that patterns of environmental injustice may diverge between the Global North and South due to differing urban development trajectories. This study uses quantitative environmental justice methods to examine spatial relationships between residential socio-demographics and industrial parks in Tijuana, Baja California Norte, Mexico using 2010 census data for Tijuana's 401 neighborhoods and municipality-provided locations of industrial parks in the city. Results of spatial lag regression models reveal that formal development is significantly associated with industrial park density, and it accounts for the significant effect of higher socioeconomic status (measured using mean education) on greater industrial density. Higher proportions of female-headed households are also significantly associated with industrial park density, while higher proportions of children and recent migrants are not. The formal development findings align with other studies in Mexico and point to the importance of urban development trajectories in shaping patterns of environmental injustice. The risks for female-headed households are novel in the Mexican context. One potential explanation is that women factory workers live near their places of employment. A second, albeit counterintuitive explanation, is the relative economic advantage experienced by female-headed households in Mexico.

African wildlife conservation has been transformed, shifting from a traditional, state-managed government approach to a broader governance approach with a wide range of actors designing and implementing wildlife policy. The most widely popularized approach has been that of community-managed nature conservancies. The knowledge of how institutions function in relation to humans and their use of the environment is critical to the design and implementation of effective conservation. This paper seeks to review the institutional and governance challenges faced in wildlife conservation in southern and eastern Africa. We discuss two different sets of challenges related to the shift in conservation practices: the practical implementation of wildlife governance, and the capacity of current governance structures to capture and distribute economic benefits from wildlife. To some extent, the issues raised by the new policies must be resolved through theoretical and empirical research addressed at wildlife conservation per se. However, many of these issues apply more broadly to a wide range of policy arenas and countries where similar policy shifts have taken place.

The impact of armed conflict on the environment is of major public policy importance. We use a geographically disaggregated dataset of civil war violence together with satellite imagery of land cover to test whether war facilitated or prevented forest loss in Sierra Leone. The conflict data set allows us to establish where rebel groups were stationed and where battles and attacks occurred. The satellite data enables to us to monitor the change in forest cover (total, primary, and secondary) in all of Sierra Leone's 151 chiefdoms, between 1990 (prior to the war) and 2000 (just prior to its end). The results suggest that conflict in Sierra Leone acted as a brake on local deforestation: conflict-ridden areas experienced significantly less forest loss relative to their more conflict-free counterparts.

The Millennium Ecosystem Assessment identified habitat loss due to the extensive growth of agriculture as the primary driver of biodiversity loss. One implication of this is that agricultural intensification has the potential to reduce threats to wild species. In this paper we consider the evidence for differences in the threat to biodiversity posed by the intensive and extensive growth of agriculture in Sub-Saharan Africa. Using data on numbers of endemic species weighted by overall threat status, we analyze the impact of agricultural productivity growth and agricultural land conversion in 27 countries on threats to mammal, bird and plant species over two time scales: one covering the period since agricultural and environmental records began, the other covering the last decade. We find that the extensive growth of agriculture is associated with increasing threats to biodiversity at all time scales. While intensification is associated with a significant reduction in the threat to all species on long time scales, however, we find that it has no significant effect on shorter time scales.

Particulate Matter (PM) pollution is one of the most important air quality concerns in Vietnam. In this study, we integrate ground-based measurements, meteorological and satellite data to map temporal PM concentrations at a 10 × 10 km grid for the entire of Vietnam. We specifically used MODIS Aqua and Terra data and developed statistically-significant regression models to map and extend the ground-based PM concentrations. We validated our models over diverse geographic provinces i.e., North East, Red River Delta, North Central Coast and South Central Coast in Vietnam. Validation suggested good results for satellite-derived PM_2.5 data compared to ground-based PM_2.5 (n = 285, r² = 0.411, RMSE = 20.299 μg m⁻³ and RE = 39.789%). Further, validation of satellite-derived PM_2.5 on two independent datasets for North East and South Central Coast suggested similar results (n = 40, r² = 0.455, RMSE = 21.512 μg m⁻³, RE = 45.236% and n = 45, r² = 0.444, RMSE = 8.551 μg m⁻³, RE = 46.446% respectively). Also, our satellite-derived PM_2.5 maps were able to replicate seasonal and spatial trends of ground-based measurements in four different regions. Our results highlight the potential use of MODIS datasets for PM estimation at a regional scale in Vietnam. However, model limitation in capturing maximal or minimal PM_2.5 peaks needs further investigations on ground data, atmospheric conditions and physical aspects.