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1.
Atmos Meas Tech ; 16(1): 169-179, 2023.
Artigo em Inglês | MEDLINE | ID: mdl-37323467

RESUMO

Low-cost sensors are often co-located with reference instruments to assess their performance and establish calibration equations, but limited discussion has focused on whether the duration of this calibration period can be optimized. We placed a multipollutant monitor that contained sensors that measure particulate matter smaller than 2.5 µm (PM2.5), carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), and nitric oxide (NO) at a reference field site for one year. We developed calibration equations using randomly selected co-location subsets spanning 1 to 180 consecutive days out of the 1-year period and compared the potential root mean square errors (RMSE) and Pearson correlation coefficients (r). The co-located calibration period required to obtain consistent results varied by sensor type, and several factors increased the co-location duration required for accurate calibration, including the response of a sensor to environmental factors, such as temperature or relative humidity (RH), or cross-sensitivities to other pollutants. Using measurements from Baltimore, MD, where a broad range of environmental conditions may be observed over a given year, we found diminishing improvements in the median RMSE for calibration periods longer than about six weeks for all the sensors. The best performing calibration periods were the ones that contained a range of environmental conditions similar to those encountered during the evaluation period (i.e., all other days of the year not used in the calibration). With optimal, varying conditions it was possible to obtain an accurate calibration in as little as one week for all sensors, suggesting that co-location can be minimized if the period is strategically selected and monitored so that the calibration period is representative of the desired measurement setting.

2.
Environ Sci Atmos ; 3(4): 683-694, 2023 Apr 13.
Artigo em Inglês | MEDLINE | ID: mdl-37063944

RESUMO

Low-cost sensors enable finer-scale spatiotemporal measurements within the existing methane (CH4) monitoring infrastructure and could help cities mitigate CH4 emissions to meet their climate goals. While initial studies of low-cost CH4 sensors have shown potential for effective CH4 measurement at ambient concentrations, sensor deployment remains limited due to questions about interferences and calibration across environments and seasons. This study evaluates sensor performance across seasons with specific attention paid to the sensor's understudied carbon monoxide (CO) interferences and environmental dependencies through long-term ambient co-location in an urban environment. The sensor was first evaluated in a laboratory using chamber calibration and co-location experiments, and then in the field through two 8 week co-locations with a reference CH4 instrument. In the laboratory, the sensor was sensitive to CH4 concentrations below ambient background concentrations. Different sensor units responded similarly to changing CH4, CO, temperature, and humidity conditions but required individual calibrations to account for differences in sensor response factors. When deployed in-field, co-located with a reference instrument near Baltimore, MD, the sensor captured diurnal trends in hourly CH4 concentration after corrections for temperature, absolute humidity, CO concentration, and hour of day. Variable performance was observed across seasons with the sensor performing well (R 2 = 0.65; percent bias 3.12%; RMSE 0.10 ppm) in the winter validation period and less accurately (R 2 = 0.12; percent bias 3.01%; RMSE 0.08 ppm) in the summer validation period where there was less dynamic range in CH4 concentrations. The results highlight the utility of sensor deployment in more variable ambient CH4 conditions and demonstrate the importance of accounting for temperature and humidity dependencies as well as co-located CO concentrations with low-cost CH4 measurements. We show this can be addressed via Multiple Linear Regression (MLR) models accounting for key covariates to enable urban measurements in areas with CH4 enhancement. Together with individualized calibration prior to deployment, the sensor shows promise for use in low-cost sensor networks and represents a valuable supplement to existing monitoring strategies to identify CH4 hotspots.

3.
Biometrics ; 79(3): 2592-2604, 2023 09.
Artigo em Inglês | MEDLINE | ID: mdl-35788984

RESUMO

Exposure to air pollution is associated with increased morbidity and mortality. Recent technological advancements permit the collection of time-resolved personal exposure data. Such data are often incomplete with missing observations and exposures below the limit of detection, which limit their use in health effects studies. In this paper, we develop an infinite hidden Markov model for multiple asynchronous multivariate time series with missing data. Our model is designed to include covariates that can inform transitions among hidden states. We implement beam sampling, a combination of slice sampling and dynamic programming, to sample the hidden states, and a Bayesian multiple imputation algorithm to impute missing data. In simulation studies, our model excels in estimating hidden states and state-specific means and imputing observations that are missing at random or below the limit of detection. We validate our imputation approach on data from the Fort Collins Commuter Study. We show that the estimated hidden states improve imputations for data that are missing at random compared to existing approaches. In a case study of the Fort Collins Commuter Study, we describe the inferential gains obtained from our model including improved imputation of missing data and the ability to identify shared patterns in activity and exposure among repeated sampling days for individuals and among distinct individuals.


Assuntos
Algoritmos , Modelos Estatísticos , Humanos , Teorema de Bayes , Fatores de Tempo , Interpretação Estatística de Dados , Simulação por Computador
4.
Ann Appl Stat ; 17(4): 3056-3087, 2023 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-38646662

RESUMO

Low-cost air pollution sensors, offering hyper-local characterization of pollutant concentrations, are becoming increasingly prevalent in environmental and public health research. However, low-cost air pollution data can be noisy, biased by environmental conditions, and usually need to be field-calibrated by collocating low-cost sensors with reference-grade instruments. We show, theoretically and empirically, that the common procedure of regression-based calibration using collocated data systematically underestimates high air pollution concentrations, which are critical to diagnose from a health perspective. Current calibration practices also often fail to utilize the spatial correlation in pollutant concentrations. We propose a novel spatial filtering approach to collocation-based calibration of low-cost networks that mitigates the underestimation issue by using an inverse regression. The inverse-regression also allows for incorporating spatial correlations by a second-stage model for the true pollutant concentrations using a conditional Gaussian Process. Our approach works with one or more collocated sites in the network and is dynamic, leveraging spatial correlation with the latest available reference data. Through extensive simulations, we demonstrate how the spatial filtering substantially improves estimation of pollutant concentrations, and measures peak concentrations with greater accuracy. We apply the methodology for calibration of a low-cost PM2.5 network in Baltimore, Maryland, and diagnose air pollution peaks that are missed by the regression-calibration.

5.
J Expo Sci Environ Epidemiol ; 32(6): 908-916, 2022 11.
Artigo em Inglês | MEDLINE | ID: mdl-36352094

RESUMO

BACKGROUND: Low-cost sensor networks for monitoring air pollution are an effective tool for expanding spatial resolution beyond the capabilities of existing state and federal reference monitoring stations. However, low-cost sensor data commonly exhibit non-linear biases with respect to environmental conditions that cannot be captured by linear models, therefore requiring extensive lab calibration. Further, these calibration models traditionally produce point estimates or uniform variance predictions which limits their downstream in exposure assessment. OBJECTIVE: Build direct field-calibration models using probabilistic gradient boosted decision trees (GBDT) that eliminate the need for resource-intensive lab calibration and that can be used to conduct probabilistic exposure assessments on the neighborhood level. METHODS: Using data from Plantower A003 particulate matter (PM) sensors deployed in Baltimore, MD from November 2018 through November 2019, a fully probabilistic NGBoost GBDT was trained on raw data from sensors co-located with a federal reference monitoring station and compared against linear regression trained on lab calibrated sensor data. The NGBoost predictions were then used in a Monte Carlo interpolation process to generate high spatial resolution probabilistic exposure gradients across Baltimore. RESULTS: We demonstrate that direct field-calibration of the raw PM2.5 sensor data using a probabilistic GBDT has improved point and distribution accuracies compared to the linear model, particularly at reference measurements exceeding 25 µg/m3, and also on monitors not included in the training set. SIGNIFICANCE: We provide a framework for utilizing the GBDT to conduct probabilistic spatial assessments of human exposure with inverse distance weighting that predicts the probability of a given location exceeding an exposure threshold and provides percentiles of exposure. These probabilistic spatial exposure assessments can be scaled by time and space with minimal modifications. Here, we used the probabilistic exposure assessment methodology to create high quality spatial-temporal PM2.5 maps on the neighborhood-scale in Baltimore, MD. IMPACT STATEMENT: We demonstrate how the use of open-source probabilistic machine learning models for in-place sensor calibration outperforms traditional linear models and does not require an initial laboratory calibration step. Further, these probabilistic models can create uniquely probabilistic spatial exposure assessments following a Monte Carlo interpolation process.


Assuntos
Poluição do Ar , Humanos , Baltimore
6.
ACS ES T Eng ; 2(5): 780-793, 2022 May 13.
Artigo em Inglês | MEDLINE | ID: mdl-35937506

RESUMO

As part of our low-cost sensor network, we colocated multipollutant monitors containing sensors for particulate matter, carbon monoxide, ozone, nitrogen dioxide, and nitrogen monoxide at a reference field site in Baltimore, MD, for 1 year. The first 6 months were used for training multiple regression models, and the second 6 months were used to evaluate the models. The models produced accurate hourly concentrations for all sensors except ozone, which likely requires nonlinear methods to capture peak summer concentrations. The models for all five pollutants produced high Pearson correlation coefficients (r > 0.85), and the hourly averaged calibrated sensor and reference concentrations from the evaluation period were within 3-12%. Each sensor required a distinct set of predictors to achieve the lowest possible root-mean-square error (RMSE). All five sensors responded to environmental factors, and three sensors exhibited cross-sensitives to another air pollutant. We compared the RMSE from models (NO2, O3, and NO) that used colocated regulatory instruments and colocated sensors as predictors to address the cross-sensitivities to another gas, and the corresponding model RMSEs for the three gas models were all within 0.5 ppb. This indicates that low-cost sensor networks can yield useable data if the monitoring package is designed to comeasure key predictors. This is key for the utilization of low-cost sensors by diverse audiences since this does not require continual access to regulatory grade instruments.

7.
Tob Control ; 2022 Jan 19.
Artigo em Inglês | MEDLINE | ID: mdl-35046128

RESUMO

RATIONALE: Tobacco outlets are concentrated in low-income neighbourhoods; higher tobacco outlet density is associated with increased smoking prevalence. Secondhand smoke (SHS) exposure has significant detrimental effects on childhood asthma. We hypothesised there was an association between higher tobacco outlet density, indoor air pollution and worse childhood asthma. METHODS: Baseline data from a home intervention study of 139 children (8-17 years) with asthma in Baltimore City included residential air nicotine monitoring, paired with serum cotinine and asthma control assessment. Participant addresses and tobacco outlets were geocoded and mapped. Multivariable regression modelling was used to describe the relationships between tobacco outlet density, SHS exposure and asthma control. RESULTS: Within a 500 m radius of each participant home, there were on average six tobacco outlets. Each additional tobacco outlet in a 500 m radius was associated with a 12% increase in air nicotine (p<0.01) and an 8% increase in serum cotinine (p=0.01). For every 10-fold increase in air nicotine levels, there was a 0.25-point increase in Asthma Therapy Assessment Questionnaire (ATAQ) score (p=0.01), and for every 10-fold increase in serum cotinine levels, there was a 0.54-point increase in ATAQ score (p<0.05). CONCLUSIONS: Increased tobacco outlet density is associated with higher levels of bedroom air nicotine and serum cotinine. Increasing levels of SHS exposure (air nicotine and serum cotinine) are associated with less controlled childhood asthma. In Baltimore City, the health of children with asthma is adversely impacted in neighbourhoods where tobacco outlets are concentrated. The implications of our findings can inform community-level interventions to address these health disparities.

8.
Ann Work Expo Health ; 66(5): 580-590, 2022 06 06.
Artigo em Inglês | MEDLINE | ID: mdl-34849566

RESUMO

Occupational exposure assessments are dominated by small sample sizes and low spatial and temporal resolution with a focus on conducting Occupational Safety and Health Administration regulatory compliance sampling. However, this style of exposure assessment is likely to underestimate true exposures and their variability in sampled areas, and entirely fail to characterize exposures in unsampled areas. The American Industrial Hygiene Association (AIHA) has developed a more realistic system of exposure ratings based on estimating the 95th percentiles of the exposures that can be used to better represent exposure uncertainty and exposure variability for decision-making; however, the ratings can still fail to capture realistic exposure with small sample sizes. Therefore, low-cost sensor networks consisting of numerous lower-quality sensors have been used to measure occupational exposures at a high spatiotemporal scale. However, the sensors must be calibrated in the laboratory or field to a reference standard. Using data from carbon monoxide (CO) sensors deployed in a heavy equipment manufacturing facility for eight months from August 2017 to March 2018, we demonstrate that machine learning with probabilistic gradient boosted decision trees (GBDT) can model raw sensor readings to reference data highly accurately, entirely removing the need for laboratory calibration. Further, we indicate how the machine learning models can produce probabilistic hazard maps of the manufacturing floor, creating a visual tool for assessing facility-wide exposures. Additionally, the ability to have a fully modeled prediction distribution for each measurement enables the use of the AIHA exposure ratings, which provide an enhanced industrial decision-making framework as opposed to simply determining if a small number of measurements were above or below a pertinent occupational exposure limit. Lastly, we show how a probabilistic modeling exposure assessment with high spatiotemporal resolution data can prevent exposure misclassifications associated with traditional models that rely exclusively on mean or point predictions.


Assuntos
Exposição Ocupacional , Saúde Ocupacional , Tomada de Decisões , Monitoramento Ambiental , Humanos , Aprendizado de Máquina , Instalações Industriais e de Manufatura , Exposição Ocupacional/análise
9.
J Occup Environ Hyg ; 18(2): 90-100, 2021 02.
Artigo em Inglês | MEDLINE | ID: mdl-33555996

RESUMO

This study describes a comprehensive exposure assessment in a stainless steel welding facility, measuring personal inhalable PM and metals, time-resolved PM10 area metals, and the bioavailable fraction of area inhalable metals. Eighteen participants wore personal inhalable samplers for two, nonconsecutive shifts. Area inhalable samplers and a time-resolved PM10 X-ray fluorescence spectrometer were used in different work areas each sampling day. Inhalable and bioavailable metals were analyzed using inductively coupled plasma mass spectrometry (ICP-MS). Median exposures to chromium, nickel, and manganese across all measured shifts were 66 (range: 13-300) µg/m3, 29 (5.7-132) µg/m3, and 22 (1.5-119) µg/m3, respectively. Most exposure variation was seen between workers ( 0.79

Assuntos
Poluentes Ocupacionais do Ar , Exposição Ocupacional , Soldagem , Poluentes Ocupacionais do Ar/análise , Monitoramento Ambiental , Humanos , Exposição Ocupacional/análise , Aço Inoxidável
10.
Atmos Environ (1994) ; 2422020 Dec 01.
Artigo em Inglês | MEDLINE | ID: mdl-32922146

RESUMO

Low-cost air pollution monitors are increasingly being deployed to enrich knowledge about ambient air-pollution at high spatial and temporal resolutions. However, unlike regulatory-grade (FEM or FRM) instruments, universal quality standards for low-cost sensors are yet to be established and their data quality varies widely. This mandates thorough evaluation and calibration before any responsible use of such data. This study presents evaluation and field-calibration of the PM2.5 data from a network of low-cost monitors currently operating in Baltimore, MD, which has only one regulatory PM2.5 monitoring site within city limits. Co-location analysis at this regulatory site in Oldtown, Baltimore revealed high variability and significant overestimation of PM2.5 levels by the raw data from these monitors. Universal laboratory corrections reduced the bias in the data, but only partially mitigated the high variability. Eight months of field co-location data at Oldtown were used to develop a gain-offset calibration model, recast as a multiple linear regression. The statistical model offered substantial improvement in prediction quality over the raw or lab-corrected data. The results were robust to the choice of the low-cost monitor used for field-calibration, as well as to different seasonal choices of training period. The raw, lab-corrected and statistically-calibrated data were evaluated for a period of two months following the training period. The statistical model had the highest agreement with the reference data, producing a 24-hour average root-mean-square-error (RMSE) of around 2 µg m -3. To assess transferability of the calibration equations to other monitors in the network, a cross-site evaluation was conducted at a second co-location site in suburban Essex, MD. The statistically calibrated data once again produced the lowest RMSE. The calibrated PM2.5 readings from the monitors in the low-cost network provided insights into the intra-urban spatiotemporal variations of PM2.5 in Baltimore.

11.
Energy Res Soc Sci ; 662020 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-32742936

RESUMO

Reducing the burden of household air pollution requires that cleaner fuels such as liquefied petroleum gas (LPG) be used nearly exclusively. However, exclusive adoption has been challenging in low- and middle-income countries. Previous studies have found that economic, social, and cultural barriers often impede adoption. We conducted in-depth qualitative interviews with 22 participants in a research trial where LPG was provided for free in Puno, Peru. We aimed to determine whether social and cultural barriers to LPG use persisted when monetary costs to the household were removed, and what factors influenced exclusive adoption of LPG in a cost-free context. Facilitators of LPG use included: support from study staff, family support, time savings, previous experience with LPG, stove design, ability to use existing pots, smoke reductions, desire for cleanliness, removal of traditional stoves, and perceptions of luck. Barriers to LPG use included: fears of LPG, problems with LPG brands, delays in obtaining LPG refills, social pressure, perceived incompatibility of traditional dishes, perceived inability to use clay pots, separate kitchens for LPG and traditional stoves, designated pots for use on the traditional stove, and lack of heat. However, these barriers did not prevent participants from using LPG nearly exclusively. Results suggest that social and cultural barriers to exclusive LPG use can be overcome when LPG stoves and fuel are provided for free and supplemented with behavioral support. Governments should evaluate the economic feasibility and sustainability of LPG subsidization, considering the potential benefits of exclusive LPG use.

12.
Atmos Environ (1994) ; 2352020 Aug 15.
Artigo em Inglês | MEDLINE | ID: mdl-32647492

RESUMO

The availability of low-cost monitors marketed for use in homes has increased rapidly over the past few years due to the advancement of sensing technologies, increased awareness of urban pollution, and the rise of citizen science. The user-friendly packages can make them appealing for use in research grade indoor exposure assessments, but a rigorous scientific evaluation has not been conducted for many monitors on the open market, which leads to uncertainty about the validity of the data. Furthermore, many previous sensor studies were conducted for a relatively short period of time, which may not capture the changes this type of instrument may exhibit over time (known as sensor aging). We evaluated three monitors (AirVisual Pro, Speck, and AirThinx) in an occupied, non-smoking residence over a 12-month period in order to assess the sensors, the built-in calibrations, and the need for additional data to achieve high accuracy for long deployments. Two units of each type of monitor were evaluated in order to assess the precision between units, and a personal DataRAM (pDR-1200) with a filter was placed in the home for about 20% of the sampling period (e.g., about a week each month) to evaluate the accuracy over time. The average PM2.5 mass concentration from the periods of colocation with the pDR were 5.31 µg/m3 for the gravimetric-corrected pDR (hereafter pDR-corrected), 5.11 and 5.03 µg/m3 for the AirVisual Pro units, 13.58 and 22.68 µg/m3 for the Speck units, and 7.56 and 7.57 µg/m3 for the AirThinx units. The AirVisual Pros exhibited the best accuracy compared to the filter at about 86%, which was slightly better than the nephelometric component of the pDR compared to the filter weight (84%). The accuracies of the Speck (-174 and -405%) and AirThinx (42 and 40%) monitors were much lower. When the 1-minute averaged PM2.5 mass concentrations were categorized by air quality index (AQI), the pDR-corrected matched the AirVisual Pro, Speck, and AirThinx bins about 97, 40, and 87% of the time, respectively. The Pearson correlation coefficients (R2) between the unit pairs and the pDR were 0.90/0.90, 0.50/0.27, and 0.92/0.93 for the AirVisual Pro, Speck, and AirThinx units, respectively. The R2 between units of the same type were 0.99, 0.17, and 1.00 for the AirVisual Pro, Speck, and AirThinx, respectively. All of the monitors could achieve better accuracy by adding filter corrections and post-processing to correct for known biases in addition to the manufacturer's correction routine. Monthly calibrations yielded the highest accuracies, but nearly as high of accuracies could be achieved with only one or two calibrations for the Air Visual Pro and the AirThinx for many applications. In general, this type of new low-cost monitor shows exciting potential for use in scientific research. However, only one of the three monitors exhibited high accuracy (within 20% of the true mass concentration) without any post processing or additional measurements, so an evaluation of each monitor is essential before the data can be used to confidently evaluate residential exposures.

13.
ALTEX ; 37(1): 3-23, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-31960937

RESUMO

Complementing the human genome with an exposome reflects the increasingly obvious impact of environmental exposure, which far exceeds the role of genetics, on human health. Considering the complexity of exposures and, in addition, the reactions of the body to exposures - i.e., the exposome - reverses classical exposure science where the precise measurement of single or few exposures is associated with specific health or environmental effects. The complete description of an individual's exposome is impossible; even less so is that of a population. We can, however, cast a wider net by foregoing some rigor in assessment and compensating with the statistical power of rich datasets. The advent of omics technologies enables a relatively cheap, high-content description of the biological effects of substances, especially in tissues and biofluids. They can be combined with many other rich data-streams, creating big data of exposure and effect. Computational methods increasingly allow data integration, discerning the signal from the noise and formulating hypotheses of exposure-effect relationships. These can be followed up in a targeted way. With a better exposure element in the risk equation, exposomics - new kid on the block of risk assessment - promises to identify novel exposure (interactions) and health/environment effect associations. This may also create opportunities to prioritize the more relevant chemicals for risk assessment, thereby lowering the burden on hazard assessment in an expo-sure-driven approach. Technological developments and synergies between approaches, quality assurance (ultimately as Good Exposome Practices), and the integration of mechanistic thinking will advance this approach.


Assuntos
Exposição Ambiental , Expossoma , Substâncias Perigosas/toxicidade , Alternativas ao Uso de Animais , Simulação por Computador , Saúde Ambiental , Regulação da Expressão Gênica/efeitos dos fármacos , Humanos , Modelos Biológicos , Medição de Risco
14.
Environ Sci Technol ; 53(2): 838-849, 2019 01 15.
Artigo em Inglês | MEDLINE | ID: mdl-30563344

RESUMO

Due to the rapid development of low-cost air-quality sensors, a rigorous scientific evaluation has not been conducted for many available sensors. We evaluated three Plantower PMS A003 sensors when exposed to eight particulate matter (PM) sources (i.e., incense, oleic acid, NaCl, talcum powder, cooking emissions, and monodispersed polystyrene latex spheres under controlled laboratory conditions and also residential air and ambient outdoor air in Baltimore, MD). The PM2.5 sensors exhibited a high degree of precision and R2 values greater than 0.86 for all sources, but the accuracy ranged from 13 to >90% compared with reference instruments. The sensors were most accurate for PM with diameters below 1 µm, and they poorly measured PM in the 2.5-5 µm range. The accuracy of the sensors was dependent on relative humidity (RH), with decreases in accuracy at RH > 50%. The sensors were able to produce meaningful data at low and high temperatures and when in motion, as it would be if utilized for outdoor or personal monitoring applications. It was most accurate in environments with polydispersed particle sources and may not be useful in specialized environments or experiments with narrow distributions of PM or aerosols with a large proportion of coarse PM.


Assuntos
Poluentes Atmosféricos , Poluição do Ar em Ambientes Fechados , Poluição do Ar , Baltimore , Monitoramento Ambiental , Tamanho da Partícula , Material Particulado
15.
Sensors (Basel) ; 18(9)2018 Sep 08.
Artigo em Inglês | MEDLINE | ID: mdl-30205550

RESUMO

Deployment of low-cost sensors in the field is increasingly popular. However, each sensor requires on-site calibration to increase the accuracy of the measurements. We established a laboratory method, the Average Slope Method, to select sensors with similar response so that a single, on-site calibration for one sensor can be used for all other sensors. The laboratory method was performed with aerosolized salt. Based on linear regression, we calculated slopes for 100 particulate matter (PM) sensors, and 50% of the PM sensors fell within ±14% of the average slope. We then compared our Average Slope Method with an Individual Slope Method and concluded that our first method balanced convenience and precision for our application. Laboratory selection was tested in the field, where we deployed 40 PM sensors inside a heavy-manufacturing site at spatially optimal locations and performed a field calibration to calculate a slope for three PM sensors with a reference instrument at one location. The average slope was applied to all PM sensors for mass concentration calculations. The calculated percent differences in the field were similar to the laboratory results. Therefore, we established a method that reduces the time and cost associated with calibration of low-cost sensors in the field.

16.
Sensors (Basel) ; 18(5)2018 May 03.
Artigo em Inglês | MEDLINE | ID: mdl-29751534

RESUMO

An integrated network of environmental monitors was developed to continuously measure several airborne hazards in a manufacturing facility. The monitors integrated low-cost sensors to measure particulate matter, carbon monoxide, ozone and nitrogen dioxide, noise, temperature and humidity. The monitors were developed and tested in situ for three months in several overlapping deployments, before a full cohort of 40 was deployed in a heavy vehicle manufacturing facility for a year of data collection. The monitors collect data from each sensor and report them to a central database every 5 min. The work includes an experimental validation of the particle, gas and noise monitors. The R² for the particle sensor ranges between 0.98 and 0.99 for particle mass densities up to 300 μg/m³. The R² for the carbon monoxide sensor is 0.99 for concentrations up to 15 ppm. The R² for the oxidizing gas sensor is 0.98 over the sensitive range from 20 to 180 ppb. The noise monitor is precise within 1% between 65 and 95 dBA. This work demonstrates the capability of distributed monitoring as a means to examine exposure variability in both space and time, building an important preliminary step towards a new approach for workplace hazard monitoring.


Assuntos
Poluentes Atmosféricos/análise , Monitoramento Ambiental/métodos , Local de Trabalho , Monóxido de Carbono/análise , Monitoramento Ambiental/economia , Monitoramento Ambiental/instrumentação , Humanos , Umidade , Instalações Industriais e de Manufatura , Dióxido de Nitrogênio/análise , Ruído Ocupacional , Ozônio/análise , Material Particulado/análise , Temperatura
17.
Environ Sci Technol ; 52(10): 6061-6069, 2018 05 15.
Artigo em Inglês | MEDLINE | ID: mdl-29697245

RESUMO

Studies of unconventional natural gas development (UNGD) and health have ranked participants along a gradient of geographic information system (GIS)-based activity that incorporated the distance between participants' home addresses and unconventional natural gas wells. However, studies have used different activity metrics, making result comparisons across the studies difficult. The existing studies have only incorporated wells, without accounting for other components of development (e.g., compressors, impoundments, and flaring events), for which it is often difficult to obtain reliable data but may have relevance to health. Our aims were to (1) describe, in space and time, UNGD-related compressors, impoundments, and flaring events; (2) evaluate whether and how to incorporate these into UNGD activity assessment; and (3) evaluate associations of these different approaches with mild asthma exacerbations. We identified 361 compressor stations, 1218 impoundments, and 216 locations with flaring events. A principal component analysis identified a single component that was approximately an equal mix of the metrics for compressors, impoundments, and four phases of well development (pad preparation, drilling, stimulation, and production). However, temporal coverage for impoundments and flaring data was sparse. Ultimately, we evaluated three UNGD activity metrics, including two based on the existing studies and a novel metric that included well pad development, drilling, stimulation, production, and compressor engine aspects of UNGD. The three metrics had varying magnitudes of association with mild asthma exacerbations, although the highest category of each metric (vs the lowest) was associated with the outcome.


Assuntos
Asma , Gás Natural , Exposição Ambiental , Humanos , Armazenamento e Recuperação da Informação , Campos de Petróleo e Gás
18.
J Occup Environ Hyg ; 15(5): 448-454, 2018 05.
Artigo em Inglês | MEDLINE | ID: mdl-29420139

RESUMO

Noise is a pervasive workplace hazard that varies spatially and temporally. The cost of direct-reading instruments for noise hampers their use in a network. The objectives for this work were to: (1) develop an inexpensive noise sensor (<$100) that measures A-weighted sound pressure levels within ±2 dBA of a Type 2 sound level meter (SLM; ∼$1,800); and (2) evaluate 50 noise sensors for use in an inexpensive sensor network. The inexpensive noise sensor consists of an electret condenser microphone, an amplifier circuit, and a microcontroller with a small form factor (28 mm by 47 mm by 9 mm) than can be operated as a stand-alone unit. Laboratory tests were conducted to evaluate 50 of the new sensors at 5 sound levels: (1) ambient sound in a quiet office; (2) 3 pink noise test signals from 65-85 dBA in 10 dBA increments; and (3) 94 dBA using a SLM calibrator. Ninety-four percent of the noise sensors (n = 46) were within ±2 dBA of the SLM for sound levels from 65-94 dBA. As sound level increased, bias decreased, ranging from 18.3% in the quiet office to 0.48% at 94 dBA. Overall bias of the sensors was 0.83% across the 75 dBA to 94 dBA range. These sensors are available for a variety of uses and can be customized for many applications, including incorporation into a stationary sensor network for continuous monitoring of noise in manufacturing environments.


Assuntos
Acústica/instrumentação , Monitoramento Ambiental/instrumentação , Ruído , Monitoramento Ambiental/economia , Ruído Ocupacional , Local de Trabalho
19.
J Occup Environ Hyg ; 15(2): 87-98, 2018 02.
Artigo em Inglês | MEDLINE | ID: mdl-29083958

RESUMO

Development of an air quality monitoring network with high spatio-temporal resolution requires installation of a large number of air pollutant monitors. However, state-of-the-art monitors are costly and may not be compatible with wireless data logging systems. In this study, low-cost electro-chemical sensors manufactured by Alphasense Ltd. for detection of CO and oxidative gases (predominantly O3 and NO2) were evaluated. The voltages from three oxidative gas sensors and three CO sensors were recorded every 2.5 sec when exposed to controlled gas concentrations in a 0.125-m3 acrylic glass chamber. Electro-chemical sensors for detection of oxidative gases demonstrated sensitivity to both NO2 and O3 with similar voltages recorded when exposed to equivalent environmental concentrations of NO2 or O3 gases, when evaluated separately. There was a strong linear relationship between the recorded voltages and target concentrations of oxidative gases (R2 > 0.98) over a wide range of concentrations. Although a strong linear relationship was also observed for CO concentrations below 12 ppm, a saturation effect was observed wherein the voltage only changes minimally for higher CO concentrations (12-50 ppm). The nonlinear behavior of the CO sensors implied their unsuitability for environments where high CO concentrations are expected. Using a manufacturer-supplied shroud, sensors were tested at 2 different flow rates (0.25 and 0.5 Lpm) to mimic field calibration of the sensors with zero air and a span gas concentration (2 ppm NO2 or 15 ppm CO). As with all electrochemical sensors, the tested devices were subject to drift with a bias up to 20% after 9 months of continuous operation. Alphasense CO sensors were found to be a proper choice for occupational and environmental CO monitoring with maximum concentration of 12 ppm, especially due to the field-ready calibration capability. Alphasense oxidative gas sensors are usable only if it is valuable to know the sum of the NO2 and O3 concentrations.


Assuntos
Monóxido de Carbono/análise , Técnicas Eletroquímicas/instrumentação , Dióxido de Nitrogênio/análise , Ozônio/análise , Poluentes Atmosféricos/análise , Técnicas Eletroquímicas/economia , Monitoramento Ambiental/economia , Monitoramento Ambiental/instrumentação
20.
Ann Appl Stat ; 11(1): 139-160, 2017 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-30100948

RESUMO

Rapid technological advances have drastically improved the data collection capacity in occupational exposure assessment. However, advanced statistical methods for analyzing such data and drawing proper inference remain limited. The objectives of this paper are (1) to provide new spatio-temporal methodology that combines data from both roving and static sensors for data processing and hazard mapping across space and over time in an indoor environment, and (2) to compare the new method with the current industry practice, demonstrating the distinct advantages of the new method and the impact on occupational hazard assessment and future policy making in environmental health as well as occupational health. A novel spatio-temporal model with a continuous index in both space and time is proposed, and a profile likelihood-based model fitting procedure is developed that allows fusion of the two types of data. To account for potential differences between the static and roving sensors, we extend the model to have nonhomogenous measurement error variances. Our methodology is applied to a case study conducted in an engine test facility, and dynamic hazard maps are drawn to show features in the data that would have been missed by existing approaches, but are captured by the new method.

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