Catastrophic impact of extreme 2019 Indonesian peatland fires on urban air quality and health.

Tropical peatland fires generate substantial quantities of airborne fine particulate matter (PM) and in Indonesia are intensified during El Niño-related drought leading to severe air quality impacts affecting local and distant populations. Limited in-situ data often necessitates reliance on air quality models, like that of the Copernicus Atmosphere Monitoring Service, whose accuracy in extreme conditions is not fully understood. Here we demonstrate how a network of low-cost sensors around Palangka Raya, Central Kalimantan during the 2019 fire season, quantified extreme air quality and city-scale variability.

Landscape fire smoke enhances the association between fine particulate matter exposure and acute respiratory infection among children under 5 years of age: Findings of a case-crossover study for 48 low- and middle-income countries.

Background: Fine particulate matter (PM) produced by landscape fires is thought to be more toxic than that from non-fire sources. However, the effects of "fire-sourced" PM on acute respiratory infection (ARI) are unknown.

Methods: We combined Demographic and Health Survey (DHS) data from 48 countries with gridded global estimates of PM concentrations from 2003 to 2014.

Systematic Mapping and Review of Landscape Fire Smoke (LFS) Exposure Impacts on Insects.

Landscape fire activity is changing in many regions because of climate change. Smoke emissions from landscape fires contain many harmful air pollutants, and beyond the potential hazard posed to human health, these also have ecological impacts. Insects play essential roles in most ecosystems worldwide, and some work suggests they may also be sensitive to smoke exposure.

Associations between exposure to landscape fire smoke and child mortality in low-income and middle-income countries: a matched case-control study.

Background: The prevalence of landscape fires has increased, particularly in low-income and middle-income countries (LMICs). We aimed to assess the impact of exposure to landscape fire smoke (LFS) on the health of children.

Methods: We conducted a sibling-matched case-control study and selected 552 155 children (aged <18 years) from Demographic and Health Surveys in 55 LMICs from 2000 to 2014.

Sensors for Fire and Smoke Monitoring.

Mastery of fire is intimately linked to advances in human civilization, culture and technology [...

Top-Down Estimation of Particulate Matter Emissions from Extreme Tropical Peatland Fires Using Geostationary Satellite Fire Radiative Power Observations.

Extreme fires in the peatlands of South East (SE) Asia are arguably the world's greatest biomass burning events, resulting in some of the worst ambient air pollution ever recorded (PM > 3000 µg·m). The worst of these fires coincide with El Niño related droughts, and include huge areas of smouldering combustion that can persist for months. However, areas of flaming surface vegetation combustion atop peat are also seen, and we show that the largest of these latter fires appear to be the most radiant and intensely smoke-emitting areas of combustion present in such extreme fire episodes.

Development of the User Requirements for the Canadian WildFireSat Satellite Mission.

In 2019 the Canadian Space Agency initiated development of a dedicated wildfire monitoring satellite (WildFireSat) mission. The intent of this mission is to support operational wildfire management, smoke and air quality forecasting, and wildfire carbon emissions reporting. In order to deliver the mission objectives, it was necessary to identify the technical and operational challenges which have prevented broad exploitation of Earth Observation (EO) in Canadian wildfire management and to address these challenges in the mission design.

Connecting Earth observation to high-throughput biodiversity data.

Understandably, given the fast pace of biodiversity loss, there is much interest in using Earth observation technology to track biodiversity, ecosystem functions and ecosystem services. However, because most biodiversity is invisible to Earth observation, indicators based on Earth observation could be misleading and reduce the effectiveness of nature conservation and even unintentionally decrease conservation effort. We describe an approach that combines automated recording devices, high-throughput DNA sequencing and modern ecological modelling to extract much more of the information available in Earth observation data.

An Overview of the Use of the SimSphere Soil Vegetation Atmosphere Transfer (SVAT) Model for the Study of Land-Atmosphere Interactions.

Soil Vegetation Atmosphere Transfer (SVAT) models consist of deterministic mathematical representations of the physical processes involved between the land surface and the atmosphere and of their interactions, at time-steps acceptable for the study of land surface processes. The present article provides a comprehensive and systematic review of one such SVAT model suitable for use in mesoscale or boundary layer studies, originally developed by [1]. This model, which has evolved significantly both architecturally and functionally since its foundation, has been widely applied in over thirty interdisciplinary science investigations, and it is currently used as a learning resource for students in a number of educational institutes globally.