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

Demographic Indicators of Reported Value of Nebraska\u27s Natural Resources

This research was conducted in various towns of eastern and central Nebraska using a survey which asked respondents about how valuable the resources and ecological benefits are that were to be involved in the construction of the Keystone XL pipeline. Surveys were administered in public locations using a combination of convenience and snowball sampling. A total of 38 respondents from the eastern region and 40 from the central region were surveyed. The research seeks to investigate if there is correlation between certain demographic categories and high value placed on the environmental facets in question. Do males or females place higher value on these resources? Do respondents who live nearer to the resources in question (those from the central region) place a higher or lower value on them? Is there a particular resource that ranks highest in value to Nebraskans? And finally, does the value place upon the ecological benefit rank higher for the respondents personally, or for their understanding of the needs of the state as a whole? Statistical analysis was conducted regarding variation between gender and region of the interviewees and no significant difference in their means was found. A more simplistic analysis of the mean valuation levels for each resource was conducted and found that respondents from Eastern Nebraska place higher value on average on every resource except agriculture

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

Smoke patterns around prescribed fires in australian eucalypt forests, as measured by low-cost particulate monitors

Prescribed burns produce smoke pollution, but little is known about the spatial and temporal pattern because smoke plumes are usually small and poorly captured by State air-quality networks. Here, we sampled smoke around 18 forested prescribed burns in the Sydney region of eastern Australia using up to 11 Nova SDS011 particulate sensors and developed a Generalised Linear Mixed Model to predict hourly PM2.5 concentrations as a function of distance, fire size and weather conditions. During the day of the burn, PM2.5 tended to show hourly exceedances (indicating poor air quality) up to ~2 km from the fire but only in the downwind direction. In the evening, this zone expanded to up to 5 km and included upwind areas. PM2.5 concentrations were higher in still, cool weather and with an unstable atmosphere. PM2.5 concentrations were also higher in larger fires. The statistical model confirmed these results, identifying the effects of distance, period of the day, wind angle, fire size, temperature and C-Haines (atmospheric instability). The model correctly identified 78% of hourly exceedance and 72% of non-exceedance values in retained test data. Applying the statistical model predicts that prescribed burns of 1000 ha can be expected to cause air quality exceedances over an area of ~3500 ha. Cool weather that reduces the risk of fire escape, has the highest potential for polluting nearby communities, and fires that burn into the night are particularly bad

Smoke Patterns around Prescribed Fires in Australian Eucalypt Forests, as Measured by Low-Cost Particulate Monitors

Prescribed burns produce smoke pollution, but little is known about the spatial and temporal pattern because smoke plumes are usually small and poorly captured by State air-quality networks. Here, we sampled smoke around 18 forested prescribed burns in the Sydney region of eastern Australia using up to 11 Nova SDS011 particulate sensors and developed a Generalised Linear Mixed Model to predict hourly PM2.5 concentrations as a function of distance, fire size and weather conditions. During the day of the burn, PM2.5 tended to show hourly exceedances (indicating poor air quality) up to ~2 km from the fire but only in the downwind direction. In the evening, this zone expanded to up to 5 km and included upwind areas. PM2.5 concentrations were higher in still, cool weather and with an unstable atmosphere. PM2.5 concentrations were also higher in larger fires. The statistical model confirmed these results, identifying the effects of distance, period of the day, wind angle, fire size, temperature and C-Haines (atmospheric instability). The model correctly identified 78% of hourly exceedance and 72% of non-exceedance values in retained test data. Applying the statistical model predicts that prescribed burns of 1000 ha can be expected to cause air quality exceedances over an area of ~3500 ha. Cool weather that reduces the risk of fire escape, has the highest potential for polluting nearby communities, and fires that burn into the night are particularly bad.</jats:p

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