Mapping pollution exposure and chemistry during an extreme air quality event (the 2018 Kilauea eruption) using a low-cost sensor network.

Extreme air quality episodes represent a major threat to human health worldwide but are highly dynamic and exceedingly challenging to monitor. The 2018 Kilauea Lower East Rift Zone eruption (May to August 2018) blanketed much of Hawai'i Island in "vog" (volcanic smog), a mixture of primary volcanic sulfur dioxide (SO2) gas and secondary particulate matter (PM). This episode was captured by several monitoring platforms, including a low-cost sensor (LCS) network consisting of 30 nodes designed and deployed specifically to monitor PM and SO2 during the event. Downwind of the eruption, network stations measured peak hourly PM2.5 and SO2 concentrations that exceeded 75 µg m-3 and 1,200 parts per billion (ppb), respectively. The LCS network's high spatial density enabled highly granular estimates of human exposure to both pollutants during the eruption, which was not possible using preexisting air quality measurements. Because of overlaps in population distribution and plume dynamics, a much larger proportion of the island's population was exposed to elevated levels of fine PM than to SO2 Additionally, the spatially distributed network was able to resolve the volcanic plume's chemical evolution downwind of the eruption. Measurements find a mean SO2 conversion time of ∼36 h, demonstrating the ability of distributed LCS networks to observe reaction kinetics and quantify chemical transformations of air pollutants in a real-world setting. This work also highlights the utility of LCS networks for emergency response during extreme episodes to complement existing air quality monitoring approaches.

Contribution of low-cost sensor measurements to the prediction of PM2.5 levels: A case study in Imperial County, California, USA.

Regulatory monitoring networks are often too sparse to support community-scale PM2.5 exposure assessment while emerging low-cost sensors have the potential to fill in the gaps. To date, limited studies, if any, have been conducted to utilize low-cost sensor measurements to improve PM2.5 prediction with high spatiotemporal resolutions based on statistical models. Imperial County in California is an exemplary region with sparse Air Quality System (AQS) monitors and a community-operated low-cost network entitled Identifying Violations Affecting Neighborhoods (IVAN). This study aims to evaluate the contribution of IVAN measurements to the quality of PM2.5 prediction. We adopted the Random Forest algorithm to estimate daily PM2.5 concentrations at a 1-km spatial resolution using three different PM2.5 datasets (AQS-only, IVAN-only, and AQS/IVAN combined). The results show that the integration of low-cost sensor measurements is an effective way to significantly improve the quality of PM2.5 prediction with an increase of cross-validation (CV) R2 by ~0.2. The IVAN measurements also contributed to the increased importance of emission source-related covariates and more reasonable spatial patterns of PM2.5. The remaining uncertainty in the calibrated IVAN measurements could still cause apparent outliers in the prediction model, highlighting the need for more effective calibration or integration methods to relieve its negative impact.

Extensive evaluation and classification of low-cost dust sensors in laboratory using a newly developed test method.

An extensive evaluation of low-cost dust sensors was performed using an exponentially decaying particle concentration. A total of 264 sensors including 27 sensors with light-emitting diodes (LEDs) and 237 sensors with laser lighting sources were tested. Those tested sensors were classified into 4 groups based on the deviation from the reference data obtained by a reference instrument. The response linearities of all the tested samples for PM1 , PM2.5 , and PM10 were in excellent agreement with the reference instrument, except a few samples. For the measurements of PM1 and PM2.5 , the lighting source, that is, LED or laser, did not show any significant difference in overall sensor performance. However, LED-based sensors did not perform well for PM10 measurements. The 32, 24, and 16% of all the tested sensors for PM1 , PM2.5 , and PM10 measurement, respectively, are in the category of Class 1 (reference instrument reading ± 20%) requirement. The performance of the low-cost dust sensors for PM10 measurement was relatively less satisfactory.

Examining the functional range of commercially available low-cost airborne particle sensors and consequences for monitoring of indoor air quality in residences.

Low-cost airborne particle sensors are gaining attention for monitoring human exposure to indoor particulate matter. This study aimed to establish the concentrations at which these commercially available sensors can be expected to report accurate concentrations. We exposed five types of commercial integrated devices and three types of "bare" low-cost particle sensors to a range of concentrations generated by three different sources. We propose definitions of upper and lower bounds of functional range based on the relationship between a given sensor's output and that of a reference instrument during a laboratory experiment. Experiments show that the lower bound can range from approximately 3 to 15 µg/m3 . At greater concentrations, sensor output deviates from linearity at approximately 300-3000 µg/m3 . We also conducted a simulation campaign to analyze the effect of this limitation on functional range on the accuracy of exposure readings given by these devices. We estimate that the upper bound results in minimal inaccuracy in exposure quantification, and the lower bound can result in as much as a 50% error in approximately 10% of US homes.

Low-Cost Sensors for Urban Noise Monitoring Networks-A Literature Review.

Noise pollution reduction in the environment is a major challenge from a societal and health point of view. To implement strategies to improve sound environments, experts need information on existing noise. The first source of information is based on the elaboration of noise maps using software, but with limitations on the realism of the maps obtained, due to numerous calculation assumptions. The second is based on the use of measured data, in particular through professional measurement observatories, but in limited numbers for practical and financial reasons. More recently, numerous technical developments, such as the miniaturization of electronic components, the accessibility of low-cost computing processors and the improved performance of electric batteries, have opened up new prospects for the deployment of low-cost sensor networks for the assessment of sound environments. Over the past fifteen years, the literature has presented numerous experiments in this field, ranging from proof of concept to operational implementation. The purpose of this article is firstly to review the literature, and secondly, to identify the expected technical characteristics of the sensors to address the problem of noise pollution assessment. Lastly, the article will also put forward the challenges that are needed to respond to a massive deployment of low-cost noise sensors.

Field performance of a low-cost sensor in the monitoring of particulate matter in Santiago, Chile.

Integration of low-cost air quality sensors with the internet of things (IoT) has become a feasible approach towards the development of smart cities. Several studies have assessed the performance of low-cost air quality sensors by comparing their measurements with reference instruments. We examined the performance of a low-cost IoT particulate matter (PM10 and PM2.5) sensor in the urban environment of Santiago, Chile. The prototype was assembled from a PM10-PM2.5 sensor (SDS011), a temperature and relative humidity sensor (BME280) and an IoT board (ESP8266/Node MCU). Field tests were conducted at three regulatory monitoring stations during the 2018 austral winter and spring seasons. The sensors at each site were operated in parallel with continuous reference air quality monitors (BAM 1020 and TEOM 1400) and a filter-based sampler (Partisol 2000i). Variability between sensor units (n = 7) and the correlation between the sensor and reference instruments were examined. Moderate inter-unit variability was observed between sensors for PM2.5 (normalized root-mean-square error 9-24%) and PM10 (10-37%). The correlations between the 1-h average concentrations reported by the sensors and continuous monitors were higher for PM2.5 (R2 0.47-0.86) than PM10 (0.24-0.56). The correlations (R2) between the 24-h PM2.5 averages from the sensors and reference instruments were 0.63-0.87 for continuous monitoring and 0.69-0.93 for filter-based samplers. Correlation analysis revealed that sensors tended to overestimate PM concentrations in high relative humidity (RH > 75%) and underestimate when RH was below 50%. Overall, the prototype evaluated exhibited adequate performance and may be potentially suitable for monitoring daily PM2.5 averages after correcting for RH.

Development and field testing of low-cost, quantal microbial assays with volunteer reporting as scalable means of drinking water safety estimation.

AIMS: To evaluate a low-cost water quality test for at-scale drinking water safety estimation in rural India. METHODS AND RESULTS: Within a longitudinal study to characterize variability in household drinking water safety in rural Maharashtra, we piloted a low-cost presence-absence (LCPA) microbial test designed to be used by volunteer residents in rural areas. In comparing the LCPA results with standard laboratory methods for enumeration of Escherichia coli, we found that LCPA tests using modified mTec media were highly sensitive in detecting drinking water of moderate risk (88% of tests were positive at E. coli counts of 11-100 CFU per 100 ml) and high risk (96% of tests were positive at E. coli counts of 101 + CFU per 100 ml). The LCPA tests demonstrated low specificity for E. coli specifically, due to concurrent detection of Klebsiella: 38% of LCPA tests were positive even when E. coli was not detected in a 100 ml sample by membrane filtration, suggesting the test would be conservative in risk estimation. We also found that 47% of participants in rural villages in India were willing to conduct tests and return results after a brief training, with 45% of active participants sending their water testing results via short message service. CONCLUSIONS: Given their low cost (~US$0.50 as piloted) and open-source format, such tests may provide a compelling alternative to standard methods for rapid water quality assessments, especially in resource-limited settings. SIGNIFICANCE AND IMPACT OF THE STUDY: The lack of availability of water quality data constrains efforts to monitor, evaluate and improve the safety of water and sanitation infrastructure in underserved settings. Current water testing methods are not scalable because of laboratory and cost constraints. Our findings indicate the LCPA or similar low-cost microbial tests could be useful in rapid water safety estimation, including via crowdsourcing.

Workplace heat exposure, health protection, and economic impacts: A case study in Canada.

BACKGROUND: Occupational heat exposure is a serious concern for worker health, productivity, and the economy. Few studies in North America assess how on-site wet bulb globe temperature (WBGT) levels and guidelines are applied in practice. METHODS: We assessed the use of a WBGT sensor for localized summertime heat exposures experienced by outdoor laborers at an industrial worksite in Ontario, Canada during the warm season (May-October) from 2012 to 2018 inclusive. We further examined informed decision making, approximated workers' predicted heat strain (sweat loss, core temperature), and estimated potential financial loss (via hourly wages) due to decreased work allowance in the heat. RESULTS: Significantly higher worksite WBGT levels occured compared with regional levels estimated at the airport, with an upward trend in heat warnings over the 7 years and expansion of warnings into the fall season. The maximum WBGT during warnings related strongly to predicted hourly sweat loss. On average, 22 hours per worker were lost each summer (~1% of annual work hours) as a result of taking breaks or stopping due to heat. This amount of time corresponded to an average individual loss of C$1100 Canadian dollars (~C$220,000 combined for ~200 workers) to workers or the company. The additional losses for an enterprise due to reduced product output were not estimated. CONCLUSIONS: Worksite observations and actions at the microscale are essential for improving the estimates of health and economic costs of extreme heat to enterprises and society. Providing worksite heat metrics to the employees aids in appropriate decision making and health protection.

Predicting future contents of soil heavy metals and related health risks by combining the models of source apportionment, soil metal accumulation and industrial economic theory.

Enriched and bio-refractory soil heavy metals (SHMs) originate from the underground mineral, which supplies energy and materials for the development of economy and industry. Investigating soil metal contents and their adverse health impacts is the principal concern associated metal contaminated industrial areas, including both current assessments and future projections. In this research, we create a novel spatiotemporal model of SHMs prediction and risk characterization for future by citing a rigorous theory of industrial economics, and time series of activity intensity changes of various pollution sources are forecasted. The dynamic change of source contributions is quantitatively resolved and the mean SHMs concentrations are estimated by classical formulas for heavy metal accumulation. Human health risk in the future is described in a manner of time series. The results of the case study show that contribution rates of the five sources of the six metals change continuously over time. Pb, Cd and As assume the highest growth rates (400%, 500% and 165%), while Zn, Ni, Cr possesses relatively lower growth (< 130%), compared to their corresponding background values. Health risk of local sensitive population (children) is estimated at exceeding threshold in 2022 (non-carcinogenic) and 2012 (carcinogenic), and the upward trend will continue. Traffic emission, agriculture and household garbage are identified as major risky sources in the coming decades at the studied area, and improvement measures are recommended. Although a degree of uncertainties exists, the overall tendency is a conservative bias for chemical risk. Additionally, this paper is the first to explore a methodology of predicting future SHMs and associated human health risk, based on industrial economics and temporal source apportionment.

Use of biological detection methods to assess dioxin-like compounds in sediments of Bohai Bay, China.

Bohai Bay, in the western region of northeastern China's Bohai Sea, receives water from large rivers containing various pollutants including dioxin-like compounds (DLCs). This study used the established zebrafish (Danio rerio) model, its known developmental toxicity endpoints and sensitive molecular analyses to evaluate sediments near and around an industrial effluent site in Bohai Bay. The primary objective was to assess the efficacy of rapid biological detection methods as an addition to chemical analyses. Embryos were exposed to various concentrations of sediment extracts as well as a 2, 3, 7, 8-tetrachlorodibenzo-p-dioxin (TCDD) positive control. Exposure to sediment extract nearest the discharge site (P1) resulted in the most severe- and highest rates of change in embryos and larvae, suggesting that DLC contaminated sediment probably did not occur much beyond it. P1 extract resulted in concentration dependent increases in mortality and pericardial edema. Its highest concentration caused up-regulation of P-450 (CYP)-1A1(CYP1A) mRNA expression at 72 h post fertilization (hpf), an increase in its expression in gill arches as observed by whole mount in situ hybridization, and an increased signal in the Tg(cyp1a: mCherry) transgenic line. The pattern and magnitude of response was very similar to that of TCDD and supported the presence of DLCs in these sediment samples. Follow-up chemical analysis confirmed this presence and identified H7CDF, O8CDF and O8CDD as the main components in P1 extract. This study validates the use of biological assays as a rapid, sensitive, and cost-effective method to evaluate DLCs and their effects in sediment samples. Additionally, it provides support for the conclusion that DLCs have limited remobilization capacity in marine sediments.

Using Six Sigma to Reduce the Utilization of Continuous Observations (1: 1) for Safety Precautions.

The need for continuous observation (1:1) of patients for safety precautions, including fall risk, elopement risk, confusion, and aggressive behavior, is highly variable, and it is therefore difficult to plan accurate staffing levels. The high variability in determining when 1:1 staffing for safety is indicated, and for how long, leads to resource strain and high cost to the hospital. A multidisciplinary team analyzed current processes for assigning, monitoring, and discontinuing safety 1:1 care for nonsuicidal patients using Six Sigma methodologies. The team implemented a standardized weaning process to reduce the duration of time on continuous observation and a standardized 4-hour reassessment using a behavior observation-tracking tool to validate the continued need for 1:1 coverage. The interventions resulted in reducing average monthly safety 1:1 staffing hours by 25.6% and saving an estimated $142 000 annually across 6 units. Phase 2 of the project integrated the observation-tracking tool and reassessment check-in into the hospital's electronic medical record for improved tracking and documentation.