In September and October 2015 widespread forest and peatland fires burned over large parts of maritime southeast Asia, most notably Indonesia, releasing large amounts of terrestrially-stored carbon into the atmosphere, primarily in the form of CO2, CO and CH4. With a mean emission rate of 11.3 Tg CO2 per day during Sept-Oct 2015, emissions from these fires exceeded the fossil fuel CO2 release rate of the European Union (EU28) (8.9 Tg CO2 per day). Although seasonal fires are a frequent occurrence in the human modified landscapes found in Indonesia, the extent of the 2015 fires was greatly inflated by an extended drought period associated with a strong El Niño. We estimate carbon emissions from the 2015 fires to be the largest seen in maritime southeast Asia since those associated with the record breaking El Niño of 1997. Compared to that event, a much better constrained regional total carbon emission estimate can be made for the 2015 fires through the use of present-day satellite observations of the fire’s radiative power output and atmospheric CO concentrations, processed using the modelling and assimilation framework of the Copernicus Atmosphere Monitoring Service (CAMS) and combined with unique in situ smoke measurements made on Kalimantan.

Deforestation and draining of the peatlands in equatorial SE Asia has greatly increased their flammability, and in September-October 2015 a strong El Niño-related drought led to further drying and to widespread burning across parts of Indonesia, primarily on Kalimantan and Sumatra. These fires resulted in some of the worst sustained outdoor air pollution ever recorded, with atmospheric particulate matter (PM) concentrations exceeding those considered "extremely hazardous to health" by up to an order of magnitude. Here we report unique in situ air quality data and tropical peatland fire emissions factors (EFs) for key carbonaceous trace gases (CO 2 , CH 4 and CO) and PM 2.5 and black carbon (BC) particulates, based on measurements conducted on Kalimantan at the height of the 2015 fires, both at locations of "pure" sub-surface peat burning and spreading vegetation fires atop burning peat. PM 2.5 are the most significant smoke constituent in terms of human health impacts, and we find in situ PM 2.5 emissions factors for pure peat burning to be 17.8 to 22.3 g·kg −1 , and for spreading vegetation fires atop burning peat 44 to 61 g·kg −1 , both far higher than past laboratory burning of tropical peat has suggested. The latter are some of the highest PM 2.5 emissions factors measured worldwide. Using our peatland CO 2 , CH 4 and CO emissions factors (1779 ± 55 g·kg −1 , 238 ± 36 g·kg −1 , and 7.8 ± 2.3 g·kg −1 respectively) alongside in situ measured peat carbon content (610 ± 47 g-C·kg −1 ) we provide a new 358 Tg (± 30%) fuel consumption estimate for the 2015 Indonesian fires, which is less than that provided by the GFEDv4.1s and GFASv1.2 global fire emissions inventories by 23% and 34% respectively, and which due to our lower EF CH4 produces far less (~3×) methane. However, our mean in situ derived EF PM2.5 for these extreme tropical peatland fires (28 ± 6 g·kg −1 ) is far higher than current emissions inventories assume, resulting in our total Remote Sens. 2018, 10, 495; doi:10.3390/rs10040495 www.mdpi.com/journal/remotesensing Remote Sens. 2018, 10, 495 2 of 31 PM 2.5 emissions estimate (9.1 ± 3.5 Tg) being many times higher than GFEDv4.1s, GFASv1.2 and FINNv2, despite our lower fuel consumption. We find that two thirds of the emitted PM 2.5 come from Kalimantan, one third from Sumatra, and 95% from burning peatlands. Using new geostationary fire radiative power (FRP) data we map the fire emissions' spatio-temporal variations in far greater detail than ever before (hourly, 0.05 • ), identifying a tropical peatland fire diurnal cycle twice as wide as in neighboring non-peat areas and peaking much later in the day. Our data show that a combination of greatly elevated PM 2.5 emissions factors, large areas of simultaneous, long-duration burning, and very high peat fuel consumption per unit area made these Sept to Oct tropical peatland fires the greatest wildfire source of particulate matter globally in 2015, furthering evidence for a regional atmospheric pollution impact whose particulate matter component in...

h i g h l i g h t s A new sampling system was designed to measure CO, CO 2, PM 2.5 and black carbon (BC) in situ during field based fires. A linear mixing model was generated to quantify the combustion phase contribution to the measurement of each species. New 'fire averaged' CO, CO 2, PM 2.5 and BC emission factors are reported for Chinese agricultural fires in different fuel types. Intense flaming processes with visible clouds of soot was found for the rapeseed residue bonfire in particular. a b s t r a c t Despite policy attempts to limit or prevent agricultural burning, its use to remove crop residues either immediately after harvest (e.g. field burning of wheat stubble) or after subsequent crop processing (e.g. "bonfires" of rice straw and rapeseed residues) appears to remain widespread across parts of China. Emission factors for these types of small but highly numerous fire are therefore required to fully assess their impact on atmospheric composition and air pollution. Here we describe the design and deployment of a new smoke measurement system for the close-range sampling of key gases and particles within smoke from crop residue fires, using it to assess instantaneous mixing ratios of CO and CO 2 and mass concentrations of black carbon (BC) and PM 2.5 from wheat stubble, rice straw, and rapeseed residue fires. Using data of our new smoke sampling system, we find a strong linear correlation between the PM 2.5 mass and BC, with very high PM 2.5 to BC emission ratios found in the smouldering phase (up to 80.7 mg m À3 .(mg m À3 ) À1 ) compared to the flaming phase (2.0 mg m À3 .(mg m À3 ) À1 ). We conclude that the contribution of BC to PM 2.5 mass was as high as 50% in the flaming phase of some burns, whilst during smouldering it sometimes decreased to little over one percent. A linear mixing model is used to quantify the relative contribution of each combustion phase to the overall measured smoke composition, and we find that flaming combustion dominated the total emission of most species assessed. Using time series of trace gas concentrations from different fire cases, we calculated 'fire integrated' trace gas emission factors (EFs) for wheat, rice and rapeseed residue burns as 1739 ± 19 g kg À1 , 1761 ± 30 g kg À1 and 1704 ± 27 g kg À1 respectively for CO 2 , and 60 ± 12 g kg À1 , 47 ± 19 g kg À1 and 82 ± 17 g kg À1 respectively for CO. Where comparisons were possible, our EFs agreed well with those derived via a simultaneously-deployed open path Fourier transform infrared (OP-FTIR) spectrometer. These EFs, and the linear best fit relationships between both PM 2.5 and BC mass and the CO 2 and CO measurements, were used to generate particulate EFs, which varied over the 5.8 e20.3 g kg À1 and 0.25e2.89 g kg À1 range respectively. We note a particularly high 2.89 g kg À1 BC emission factor for the rapeseed bonfires, reflective of intense flaming combustion that gave off visible clouds of soot. These field-measured EFs offer a different perspective than is obtained when burning in laboratory combustion chambe...

Repeat observations underpin our understanding of environmental processes, but financial constraints often limit scientists’ ability to deploy dense networks of conventional commercial instrumentation. Rapid growth in the Internet-Of-Things (IoT) and the maker movement is paving the way for low-cost electronic sensors to transform global environmental monitoring. Accessible and inexpensive sensor construction is also fostering exciting opportunities for citizen science and participatory research. Drawing on 6 years of developmental work with Arduino-based open-source hardware and software, extensive laboratory and field testing, and incorporation of such technology into active research programmes, we outline a series of successes, failures and lessons learned in designing and deploying environmental sensors. Six case studies are presented: a water table depth probe, air and water quality sensors, multi-parameter weather stations, a time-sequencing lake sediment trap, and a sonic anemometer for monitoring sand transport. Schematics, code and purchasing guidance to reproduce our sensors are described in the paper, with detailed build instructions hosted on our King’s College London Geography Environmental Sensors Github repository and the FreeStation project website. We show in each case study that manual design and construction can produce research-grade scientific instrumentation (mean bias error for calibrated sensors –0.04 to 23%) for a fraction of the conventional cost, provided rigorous, sensor-specific calibration and field testing is conducted. In sharing our collective experiences with build-it-yourself environmental monitoring, we intend for this paper to act as a catalyst for physical geographers and the wider environmental science community to begin incorporating low-cost sensor development into their research activities. The capacity to deploy denser sensor networks should ultimately lead to superior environmental monitoring at the local to global scales.

Repeat observations underpin our understanding of environmental processes but financial constraints often limit scientists’ ability to deploy dense networks of conventional commercial instrumentation. Rapid growth in the Internet-Of-Things (IOT) and the maker movement is paving the way for low-cost electronic sensors to transform global environmental monitoring. Accessible and inexpensive sensor construction is also fostering exciting opportunities for citizen science and participatory research. Drawing on six years of developmental work with Arduino open-source hardware and software and active field research, we outline a series of successes, failures and lessons learned in designing and deploying environmental sensors. Six case studies are presented: a water table depth probe, air and water quality sensors, multi-parameter weather stations, a time-sequencing lake sediment trap and a sonic anemometer for monitoring sand transport. Sensor design and schematics are described alongside an evaluation of pitfalls and future improvements for individual sensors and the workflow process. We show that manual design and construction can produce research-grade scientific instruments for a fraction of the conventional cost. In sharing our collective experiences with build-it-yourself environmental monitoring, we intend for this paper to act as a platform for scientists and educators to delve into low-cost sensor development. This will ultimately lead to superior environmental monitoring at higher spatial and temporal resolution from the local to global scales.