Traffic exhaust to wildfires: PM2.5 measurements with fixed and portable, low-cost LoRaWAN-connected sensors

© 2020 Forehead et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Air pollution with PM2.5 (particulate matter smaller than 2.5 micro-metres in diameter) is a major health hazard in many cities worldwide, but since measuring instruments have traditionally been expensive, monitoring sites are rare and generally show only background concentrations. With the advent of low-cost, wirelessly connected sensors, air quality measurements are increasingly being made in places where many people spend time and pollution is much worse: on streets near traffic. In the interests of enabling members of the public to measure the air that they breathe, we took an open-source approach to designing a device for measuring PM2.5. Parts are relatively cheap, but of good quality and can be easily found in electronics or hardware stores, or on-line. Software is open source and the free LoRaWAN-based “The Things Network” the platform. A number of low-cost sensors we tested had problems, but those selected performed well when co-located with reference-quality instruments. A network of the devices was deployed in an urban centre, yielding valuable data for an extended time. Concentrations of PM2.5 at street level were often ten times worse than at air quality stations. The devices and network offer the opportunity for measurements in locations that concern the public

Air quality near busy Australian roads up to 10 times worse than official figures

Air quality on Australia’s roads matters. On any given day (when we’re not in lockdown) people meet, commute, exercise, shop and walk with children near busy streets. But to date, air quality monitoring at roadsides has been inadequate. I and my colleagues wanted to change that. Using materials purchased from electronics and hardware stores for around A$150, we built our own air quality monitors. Our newly published research reveals how our devices detected particulate pollution at busy intersections at levels ten times worse than background levels measured at official air monitoring stations

Review of modelling air pollution from traffic at street-level - The state of the science

Traffic emissions are a complex and variable cocktail of toxic chemicals. They are the major source of atmospheric pollution in the parts of cities where people live, commute and work. Reducing exposure requires information about the distribution and nature of emissions. Spatially and temporally detailed data are required, because both the rate of production and the composition of emissions vary significantly with time of day and with local changes in wind, traffic composition and flow. Increasing computer processing power means that models can accept highly detailed inputs of fleet, fuels and road networks. The state of the science models can simulate the behaviour and emissions of all the individual vehicles on a road network, with resolution of a second and tens of metres. The chemistry of the simulated emissions is also highly resolved, due to consideration of multiple engine processes, fuel evaporation and tyre wear. Good results can be achieved with both commercially available and open source models. The extent of a simulation is usually limited by processing capacity; the accuracy by the quality of traffic data. Recent studies have generated real time, detailed emissions data by using inputs from novel traffic sensing technologies and data from intelligent traffic systems (ITS). Increasingly, detailed pollution data is being combined with spatially resolved demographic or epidemiological data for targeted risk analyses

Investigating the accuracy of georeferenced social media data for flood mapping: The PetaJakarta.org case study

Georeferenced social media data are gaining increased application in creating near real-time flood maps needed to improve situational awareness in data-starved regions. However, there is growing concern that the georeferenced locations of flood-related social media contents do not always correspond to the actual locations of the flooding event. But to what extent is this true? Without this knowledge, it is difficult to ascertain the accuracy of flood maps created using georeferenced social media contents. This study aims to improve understanding of the extent to which georeferenced locations of social media flood reports deviate from the actual locations of floods. The study analyses flood-related tweets acquired as part of the PetaJakarta.org project implemented in the coastal mega-city of Jakarta and provides insight into the level of accuracy expected with using georeferenced social media data for flood mapping. Importantly, the results reveal that the accuracy of flood maps generated with georeferenced social media data reduces with increase in the size of the minimum mapping unit of the flood map. Finally, an approach is recommended for creating more accurate real time flood maps from crowdsourced social media data

Microbial communities of subtidal shallow sandy sediments change with depth and wave disturbance, but nutrient exchanges remain similar

Along 3 replicate transects, sediments were sampled from a subtidal sandbank in Cockburn Sound, Western Australia, at 4 depths: 1.5, 4 and 8 m and at 14 m on the flat at the base of the bank. Pulse amplitude modulated (PAM) fluorescence, fluxes of oxygen and inorganic nutrients, N2 fixation and denitrification were measured and sediments analysed for granulometry, pigments, fatty acids, neutral lipids, organic C and total N. There were 2 functional depth zones: 1.5 ~ \u3c4, and ≥4 m. At 1.5 m, chl a concentration was 42.3 mg m–2 (1.83 SE, n = 12), sediments were net heterotrophic, and there were effluxes of inorganic nutrients in the light and uptake in the dark. The 2 intermediate depths had benthic microalgal (BMA) biomass around 88 mg m–2 chl a, and mean gross primary productivity of 2.23 mmol O2 m–2 h–1. At 14 m, chl a concentration was 75 mg m–2, and sediments were net autotrophic. Sediment–water exchanges of inorganic nutrients were dominated by NH4, with maximum efflux from the sediment (1044 µmol m–2 d–1) at 8 m and maximum uptake (539 µmol m–2 d–1) at 4 m. At 1.5 m depth, there was a marked discontinuity in most parameters as the microbial community metabolism and cycling of nutrients between the sediment and water column were altered in conditions of more frequent wave disturbance. At depths ≥4 m, we observed greater amounts of biomass and more primary productivity, but net exchanges of inorganic nutrients were remarkably consistent at all depths from 1.5 to 14 m

Small doses, big troubles: Modeling growth dynamics of organisms affecting microalgal production cultures in closed photobioreactors

The destruction of mass cultures of microalgae by biological contamination of culture medium is a pervasive and expensive problem, in industry and research. A mathematical model has been formulated that attempts to explain contaminant growth dynamics in closed photobioreactors (PBRs). The model simulates an initial growth phase without PBR dilution, followed by a production phase in which culture is intermittently removed. Contaminants can be introduced at any of these stages. The model shows how exponential growth from low initial inocula can lead to explosive growth in the population of contaminants, appearing days to weeks after inoculation. Principal influences are contaminant growth rate, PBR dilution rate, and the size of initial contaminant inoculum. Predictions corresponded closely with observed behavior of two contaminants, Uronema sp. and Neoparamoeba sp., found in operating PBRs. A simple, cheap and effective protocol was developed for short-term prediction of contamination in PBRs, using microscopy and archived sample

Crowdsourced social media data for disaster management: Lessons from the PetaJakarta.org project

The application of crowdsourced social media data in flood mapping and other disaster management initiatives is a burgeoning field of research, but not one that is without challenges. In identifying these challenges and in making appropriate recommendations for future direction, it is vital that we learn from the past by taking a constructively critical appraisal of highly-praised projects in this field, which through real-world implementations have pioneered the use of crowdsourced geospatial data in modern disaster management. These real-world applications represent natural experiments, each with myriads of lessons that cannot be easily gained from computer-confined simulations. This paper reports on lessons learnt from a 3-year implementation of a highly-praised project- the PetaJakarta.org project. The lessons presented derive from the key success factors and the challenges associated with the PetaJakarta.org project. To contribute in addressing some of the identified challenges, desirable characteristics of future social media-based disaster mapping systems are discussed. It is envisaged that the lessons and insights shared in this study will prove invaluable within the broader context of designing socio-technical systems for crowdsourcing and harnessing disaster-related information

Shifts in composition of microbial communities of subtidal sandy sediments maximise retention of nutrients

The density and composition of microbial communities of subtidal sandy sediments determines their role in the cycling of nutrients in coastal waters. It has previously been found that sediments disturbed by waves and currents have reduced biomass, greater productivity to respiration (P/R) ratios and a tendency to take up nutrients. Conversely, with shelter and greater biomass, P/R ratios were smaller and nutrients released. This study, in warm temperate waters, examined the consequences of high and low levels of hydrodynamic energy on the microbial community structure and biogeochemistry at two locations at different times of year. Measurements included biomarkers, sediment properties and exchanges of gases and nutrients. Microbial communities were dominated by diatoms and bacteria. Exposed sites, relative to paired sheltered sites, had smaller ratios of bacteria to benthic microalgae (BMA), larger C/N ratios, smaller indices of diagenetic activity, but smaller P/R ratios. The bacteria in exposed sediments exhibited biomass-normalised rates of respiration almost double those in sheltered sediments. This increased activity was most likely fuelled by elevated concentrations of photosynthates, secreted by BMA attached to sand grains. Changes in community composition owing to different levels of disturbance led to shifts in functioning that resulted in consistently small exchanges of nutrients

Effects of shelter and enrichment on the ecology and nutrient cycling of microbial communities of subtidal carbonate sediments

The interactions between physical disturbances and biogeochemical cycling are fundamental to ecology. The benthic microbial community controls the major pathway of nutrient recycling in most shallow-water ecosystems. This community is strongly influenced by physical forcing and nutrient inputs. Our study tests the hypotheses that benthic microbial communities respond to shelter and enrichment with (1) increased biomass, (2) change in community composition and (3) increased uptake of inorganic nutrients from the water column. Replicate in situ plots were sheltered from physical disturbance and enriched with inorganic nutrients or left without additional nutrients. At t 0 and after 10 days, sediment-water fluxes of nutrients, O 2 and N 2, were measured, the community was characterized with biomarkers. Autochthonous benthic microalgal (BMA) biomass increased 30% with shelter and a natural fivefold increase in nutrient concentration; biomass did not increase with greater enrichment. Diatoms remained the dominant taxon of BMA, suggesting that the sediments were not N or Si limited. Bacteria and other heterotrophic organisms increased with enrichment and shelter. Daily exchanges of inorganic nutrients between sediments and the water column did not change in response to shelter or nutrient enrichment. In these sediments, physical disturbance, perhaps in conjunction with nutrient enrichment, was the primary determinant of microbial biomass

Participation Patterns and Reliability of Human Sensing in Crowd-Sourced Disaster Management

Over the last ten years, there has been a significant increase in crowd-sourcing applications for disaster management. Their success depends heavily on the behaviour of social media users, acting as human sensors during disaster monitoring and emergency response. Unlike their technological counterparts, human sensors are complex social entities, contributing in different ways to their collective task and creating varying participation patterns through social media. Failing to understand these participation patterns limits our capacity to evaluate the reliability of human sensing in different contexts. Based on an analysis of flood-related information contributed by Twitter users in Jakarta during the 2014/2015 and 2015/2016 monsoonal seasons, this study establishes four categories of human sensors and their respective levels of reliability for disaster management. The results have significant implications for how we frame expectations and develop reliance on the use of social media for disaster management. Importantly, the results will serve as a useful guide for understanding levels of incentive that may be required to motivate members of the different categories of social media users during emergencies and disasters