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We evaluated the uncertainty associated with secondary pollutants formation due to different chemical mechanisms in photochemical modelling. The CMAQ modelling system was utilized in conjunction with CB6R3, SAPRC07, and RACM2 chemical mechanisms and compared the concentrations of various chemical species, including ozone (O3) and particulate matter (PM2.5). Using datasets from ambient monitoring stations, we assessed the performance of each of the mechanism in summer and winter. The concentrations of various chemical species predicted by the three mechanisms varied significantly. The differences are more evident in summer than in winter for most of the species, except for hydrogen peroxide (H2O2), methyl hydroperoxide (MEPX), and Secondary Organic Aerosol—Anthropogenic. We observed that the summer daytime O3 predictions showed reasonable peaks at the three air quality monitoring sites, but the nighttime values under-predicted for all three mechanisms. In the winter, all three mechanisms tend to under-predict O3. Differences in the mean O3 values (bias) at the different sites, for the different seasons, are consistent with corrections made to previous modelling studies that modified KZMIN. PM2.5 predictions with RACM2 were slightly better. The dominant PM2.5 species in summer were sulfate and SOA-Bio, which may be attributed to non-mobile sources in the region, while NO3 became dominant in winter due to more favorable conditions for forming this species, including lower temperatures and an elevated NH4 to SO4 ratio. We concluded that the differences in O3 and PM2.5 predictions between the three mechanisms are significant, implying that when developing strategic and management actions are based on modelling, the most appropriate mechanism should be considered.

Quantification of Land‐Use and Land‐Cover (LULC) changes associated with energy extraction footprints plays an important role in reclamation performance assessment, compliance monitoring, and sustainable land management practices. For regulatory planning and decision making purposes, it is crucial to understand the types and representation of disturbance present on the landscape related to energy footprints (i.e., oil, gas, and coal mining activities), non-energy footprints (i.e., other infrastructure), cutblocks, and wildfire. In this study, Landsat multispectral datasets were analysed using advanced classification and unmixing algorithms to calculate LULC changes associated with these disturbance agents from 1985 to 2015 in the Upper Peace Region, one of the major oil and gas exploration sites in Alberta, Canada. Based on 5-year intervals throughout this 30-year study, seven epochal LULC maps and a cumulative land disturbance map were produced with 82 % and 89 % overall accuracies, respectively, based on extensive and multisource, independent validation. Assessment of multiple disturbance regimes and time-series trajectories with high accuracies in this complex environment was vital to document and quantify disturbance, vegetation re-establishment, succession, and reclamation. Results indicate that more than 60 % forest loss (coniferous and deciduous) occurred due to harvesting and less than 20 % forest loss occurred due to energy and non‐energy footprints. An assessment of the current state showed 40–60 % of cutblocks and burned areas returned as immature forest and wetland and 15–20 % recovered to forest, accounting for successional processes that take time for the re-establishment of trees following disturbance. Out of total energy footprints, 8 % recovered as shrubland and 16 % recovered as forest, with almost no sign of vegetation recovery evident in non‐energy footprints (e.g., urban and long term industrial land use). The highest area of land disturbance (3412 km2) was over coniferous forest with 5-year disturbance areas of 55 km2, 65 km2, 70 km2, and 295 km2, associated with, respectively, wildfire, energy development, non‐energy footprints, and harvesting. This type of LULC investigation informs land‐use threshold management for energy footprints by identifying and partitioning key disturbance agents into land use. Knowledge of change type and land use enables insights on long term implications of the changes present and serves to guide sustainable development, environmental protection, and informing forward looking land use future state simulations.

An increase in the frequency of severe fire events, as well as a growing interest in wildfire mitigation strategies, has created a demand for skilled managers of landscape fire and a better community understanding of fire behaviour. While on-ground experience is essential, there is potential to substantially enhance training and community engagement with explanatory simulations. Through this work, we explore landscape fire behaviour as a complex system where understanding key behaviour characteristics is often more important and achievable than prediction. It is argued that this approach has particular value in Northern Australia, where fires burn across vast and sparsely inhabited landscapes that are largely under Indigenous ownership. Land and fire management in such complex cross-cultural contexts requires combining traditional and local knowledge with science and technology to achieve the best outcomes. We describe the workings of the model, a stochastic cellular automata fire behaviour simulation, developed through a participatory modelling approach for Northern Australia; the outputs generated; and a range of operational applications. We found that simulation assisted training and engagement through the development of an understanding of fire dynamics through visualisation, underpinned by landscape data sets, and engaging a culturally diverse set of land managers in discussions of fire management. We conclude that there is scope for a broader use of explanatory fire simulations to support development of shared understandings of fire management objectives.

The science of ecosystem service (ES) mapping has become increasingly sophisticated over the past 20 years, and examples of successfully integrating ES into management decisions at national and sub-national scales have begun to emerge. However, increasing model sophistication and accuracy—and therefore complexity—may trade-off with ease of use and applicability to real-world decision-making contexts, so it is vital to incorporate the lessons learned from implementation efforts into new model development. Using successful implementation efforts for guidance, we developed an integrated ES modelling system to quantify several ecosystem services: forest timber production and carbon storage, water purification, pollination, and biodiversity. The system is designed to facilitate uptake of ES information into land-use decisions through three principal considerations: (1) using relatively straightforward models that can be readily deployed and interpreted without specialized expertise; (2) using an agent-based modelling framework to enable the incorporation of human decision-making directly within the model; and (3) integration among all ES models to simultaneously demonstrate the effects of a single land-use decision on multiple ES. We present an implementation of the model for a major watershed in Alberta, Canada, and highlight the system’s capabilities to assess a suite of ES under future management decisions, including forestry activities under two alternative timber harvest strategies, and through a scenario modelling analysis exploring different intensities of hypothetical agricultural expansion. By using a modular approach, the modelling system can be readily expanded to evaluate additional ecosystem services or management questions of interest in order to guide land-use decisions to achieve socioeconomic and environmental objectives.

Many contemporary societal challenges manifest themselves in the domain of human-environment interactions. There is a growing recognition that responses to these challenges formulated within current disciplinary boundaries, in isolation from their wider contexts, cannot adequately address them. Here, we outline the need for an integrated, transdisciplinary synthesis that allows for a holistic approach, and, above all, a much longer time perspective. We outline both the need for and the fundamental characteristics of what we call "integrated history." This approach promises to yield new understandings of the relationship between the past, present, and possible futures of our integrated human-environment system. We recommend a unique new focus of our historical efforts on the future, rather than the past, concentrated on learning about future possibilities from history. A growing worldwide community of transdisciplinary scholars is forming around building this Integrated History and future of People on Earth (IHOPE). Building integrated models of past human societies and their interactions with their environments yields new insights into those interactions and can help to create a more sustainable and desirable future. The activity has become a major focus within the global change community.