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Traditional air monitoring approaches using regulatory monitors have historically been used to assess regional-scale trends in air pollutants across large geographical areas. Recent advances in air pollution sensor technologies could provide additional information about nearby sources, support the siting of regulatory monitoring stations, and improve our knowledge of finer-scale spatiotemporal variation of ambient air pollutants and their associated health effects. Sensors are now being developed that are much smaller and lower cost than traditional ambient air monitoring systems and are capable of being deployed as a network to provide greater coverage of a given area. The CitySpace project conducted by the US EPA and the Shelby County Health Department included the deployment of a network of 17 sensor pods using Alphasense OPC-N2 particulate matter (PM) sensors integrated with meteorological sensors in Memphis, TN for six months. Sensor pods were collocated with a federal equivalent method (FEM) tapered element oscillating microbalance (TEOM) monitor both before and after the primary study period. Six of the sensor pods were found to meet the data quality objective (DQO) of coefficient of determination (R2) greater than 0.5 when collocated with the TEOM. Seven pods were decommissioned before the end of the study due to mechanical failure. The six pods meeting the DQO were used to examine the spatiotemporal variability of fine PM (PM2.5) across the Memphis area. One site was found to have higher relative PM2.5 concentrations when compared to the other sites in the network. The 1-min data from this sensor pod were evaluated to quantify the regional urban background and local-scale contributions to PM2.5 at that monitoring location. This method found that approximately 20% of the PM2.5 was attributed to local sources at this location, compared to 9% at a local regulatory monitoring site. Additionally, the 1-min data were combined with 1-min wind speed and wind direction data to examine potential sources in the area using the nonparametric trajectory analysis (NTA) technique. This method geographically identified local source areas that contributed to the measured concentrations at the high reading sensor location throughout the course of the study.

Air pollution epidemiology studies of ambient fine particulate matter (PM2.5) often use outdoor concentrations as exposure surrogates, which can induce exposure error. The goal of this study was to improve ambient PM2.5 exposure assessments for a repeated measurements study with 22 diabetic individuals in central North Carolina called the Diabetes and Environment Panel Study (DEPS) by applying the Exposure Model for Individuals (EMI), which predicts five tiers of individual-level exposure metrics for ambient PM2.5 using outdoor concentrations, questionnaires, weather, and time-location information. Using EMI, we linked a mechanistic air exchange rate (AER) model to a mass-balance PM2.5 infiltration model to predict residential AER (Tier 1), infiltration factors (F-inf_home, Tier 2), indoor concentrations (C-in, Tier 3), personal exposure factors (F-pex, Tier 4), and personal exposures (E, Tier 5) for ambient PM2.5. We applied EMI to predict daily PM2.5 exposure metrics (Tiers 1-5) for 174 participant-days across the 13 months of DEPS. Individual model predictions were compared to a subset of daily measurements of F-pex and E (Tiers 4-5) from the DEPS participants. Model-predicted Fpex and E corresponded well to daily measurements with a median difference of 14% and 23%; respectively. Daily model predictions for all 174 days showed considerable temporal and house-to-house variability of AER, F-inf_home, and C-in (Tiers 1-3), and person-to-person variability of F-pex and E (Tiers 4-5). Our study demonstrates the capability of predicting individual-level ambient PM2.5 exposure metrics for an epidemiological study, in support of improving risk estimation. (C) 2018 Published by Elsevier B.V.

The United States Environmental Protection Agency held an international two-day workshop in June 2018 to deliberate possible performance targets for non-regulatory fine particulate matter (PM2.5) and ozone (O3) air sensors. The need for a workshop arose from the lack of any market-wide manufacturer requirement for documented sensor performance evaluations, the lack of any independent third party or government-based sensor performance certification program, and uncertainty among all users as to the general usability of air sensor data. A multi-sector subject matter expert panel was assembled to facilitate an open discussion on these issues with multiple stakeholders. This summary provides an overview of the workshop purpose, key findings from the deliberations, and considerations for future actions specific to sensors. Important findings concerning PM2.5 and O3 sensors included the lack of consistent performance indicators and statistical metrics as well as highly variable data quality requirements depending on the intended use. While the workshop did not attempt to yield consensus on any topic, a key message was that a number of possible future actions would be beneficial to all stakeholders regarding sensor technologies. These included documentation of best practices, sharing quality assurance results along with sensor data, and the development of a common performance target lexicon, performance targets, and test protocols. Keywords: Low-cost air quality sensors, Performance targets, PM2.5, Ozone

Advances in air pollution sensor technology have enabled the development of small and low-cost systems to measure outdoor air pollution. The deployment of a large number of sensors across a small geographic area would have potential benefits to supplement traditional monitoring networks with additional geographic and temporal measurement resolution, if the data quality were sufficient. To understand the capability of emerging air sensor technology, the Community Air Sensor Network (CAIRSENSE) project deployed low-cost, continuous, and commercially available air pollution sensors at a regulatory air monitoring site and as a local sensor network over a surrounding ∼ 2 km area in the southeastern United States. Collocation of sensors measuring oxides of nitrogen, ozone, carbon monoxide, sulfur dioxide, and particles revealed highly variable performance, both in terms of comparison to a reference monitor as well as the degree to which multiple identical sensors produced the same signal. Multiple ozone, nitrogen dioxide, and carbon monoxide sensors revealed low to very high correlation with a reference monitor, with Pearson sample correlation coefficient (r) ranging from 0.39 to 0.97, −0.25 to 0.76, and −0.40 to 0.82, respectively. The only sulfur dioxide sensor tested revealed no correlation (r r = 0.99) between identical sensors but moderate agreement with a reference PM2.5 monitor (e.g., r = 0.65). For select sensors that had moderate to strong correlation with reference monitors (r > 0.5), step-wise multiple linear regression was performed to determine if ambient temperature, relative humidity (RH), or age of the sensor in number of sampling days could be used in a correction algorithm to improve the agreement. Maximum improvement in agreement with a reference, incorporating all factors, was observed for an NO2 sensor (multiple correlation coefficient R2adj-orig = 0.57, R2adj-final = 0.81); however, other sensors showed no apparent improvement in agreement. A four-node sensor network was successfully able to capture ozone (two nodes) and PM (four nodes) data for an 8-month period of time and show expected diurnal concentration patterns, as well as potential ozone titration due to nearby traffic emissions. Overall, this study demonstrates the performance of emerging air quality sensor technologies in a real-world setting; the variable agreement between sensors and reference monitors indicates that in situ testing of sensors against benchmark monitors should be a critical aspect of all field studies.

Background An adult cohort consisting of 63 participants engaged in the US EPA's recent Detroit Exposure and Aerosol Research Study (DEARS) and a University of Michigan cardiovascular sub-study conducted during summer and winter periods over 3 years between 2004 and 2007 (5 seasons in total). Through all participants' wearing of a monitoring vest, personal exposures to various air pollutants were measured. Purpose The study objective was to identify the association between personal exposure to volatile organic compounds (VOCs) and six cardiovascular health endpoints in an adult non-smoking cohort of the DEARS. Methods Twenty five VOCs were collected using the DEARS exposure vest incorporating advanced passive diffusion tube. Six cardiovascular health endpoints including systolic and diastolic blood pressure (SBP, DBP), heart rate (HR), brachial artery diameter (BAD), brachial artery flow-mediated dilatation (FMD) and nitroglycerin-mediated arterial dilatation (NMD) were collected by novel, in-home clinical examinations. To reduce the number of personal VOCs highly correlated to each other, a principal component analysis was conducted. Accounting for more individual variations in association between personal VOCs and cardiovascular health endpoints, a linear mixed model was employed, where cohort subjects were not necessarily to have the same linear association. Results Applying the principal component analysis, 3 out of 12 components were retained, which appeared to involve a petroleum source (1st component), a 1-3 butadiene source (2nd component), and an ambient (Freon) source (3rd component). Petroleum related VOCs were associated with increases in FMD and showed mixed relationships with NMD (lag 0–1 day increased NMD, lag 2 days decreased NMD). Butadiene related VOCs decreased DBP but increased HR and BAD. Freon (ambient background) related VOCs increased HR. Conclusions We observed mixed and variable results in this first study to evaluate the relationships between personal exposures to VOCs of different origin on cardiovascular physiology. In sum, the findings suggest that VOCs may have rapid impacts upon the human cardiovascular system; however, understanding the health implications and the mechanisms responsible is beyond the scope of this investigation.

Air pollution sensors are quickly proliferating for use in a wide variety of applications, with a low price point that supports use in high-density networks, citizen science, and individual consumer use. This emerging technology motivates the assessment under real-world conditions, including varying pollution levels and environmental conditions. A seven-month, systematic field evaluation of low-cost air pollution sensors was performed in Denver, Colorado, over 2015–2016; the location was chosen to evaluate the sensors in a high-altitude, cool, and dry climate. A suite of particulate matter (PM), ozone (O3), and nitrogen dioxide (NO2) sensors were deployed in triplicate and were collocated with federal equivalent method (FEM) monitors at an urban regulatory site. Sensors were evaluated for their data completeness, correlation with reference monitors, and ability to reproduce trends in pollution data, such as daily concentration values and wind-direction patterns. Most sensors showed high data completeness when data loggers were functioning properly. The sensors displayed a range of correlations with reference instruments, from poor to very high (e.g., hourly-average PM Pearson correlations with reference measurements varied from 0.01 to 0.86). Some sensors showed a change in response to laboratory audits/testing from before the sampling campaign to afterwards, such as Aeroqual, where the O3 response slope changed from about 1.2 to 0.6. Some PM sensors measured wind-direction and time-of-day trends similar to those measured by reference monitors, while others did not. This study showed different results for sensor performance than previous studies performed by the U.S. EPA and others, which could be due to different geographic location, meteorology, and aerosol properties. These results imply that continued field testing is necessary to understand emerging air sensing technology.

Background Mold exposures have been linked to the development and exacerbation of asthma. The purpose of this study was to determine whether the Environmental Relative Moldiness Index (ERMI) metric, developed to quantify mold exposures in homes, might be applied to evaluating the mold contamination in schools. Methods Settled dust samples (n = 10) were collected on each level of a water-damaged school in Springfield, Massachusetts and two samples per level in five Idaho schools. Each dust sample was analyzed for the 36 molds that make up the ERMI. The concentration of 2.5-μm particulate matter (PM2.5) was measured in each school at two locations during the spring of 2013. Results The average ERMI value in the Springfield school, 15.51, was significantly greater (p

Throughout the 50 years of the Quarterly Journal of Engineering Geology and Hydrogeology papers have been published on case studies, site characterization and material geotechnical properties that have been influenced by the climatic and geomorphological environments experienced during the Quaternary. This period of geological time has left a significant legacy for ground engineering and construction projects with a complexity of ground conditions whose interpretation and understanding are crucial for the success of any such schemes. This review presents key papers that mainly focus on the effects of cold climates, both glacial and periglacial, developed during this time. Advances in landsystem description and ground model development published in the journal for these terrains are discussed.

Air monitoring is desirable in many places to understand dynamic pollution trends and sources and improve knowledge of population exposure. While highly miniaturized low cost sensor technology is quickly evolving, there is also a need for the advancement of mid-tier systems that are closer to reference-grade technologies in their longevity and performance, but also feature compactness that requires less significant infrastructure. This project evaluated the performance of a prototype solar-powered air monitoring system known as a Village Green Project (VGP) system with wireless data transmission that was deployed on a school rooftop in Hong Kong and operated for over one year. The system provided highly time-resolved and long-term data utilizing mid-tier cost ozone, PM2.5 and meteorological instruments. It operated with very minimal maintenance but shading by a nearby building reduced solar radiation, thus battery run time, over the 16-months measurement period, approximately 330,000 1-min observations were recorded (data completeness of ~62%). The monitoring data were evaluated by comparison with a nearby Hong Kong Environment Protection Department (EPD) station and exhibited good performance for 1-h resolution (R2 = 0.74 for PM2.5 and R2 = 0.76 for ozone). Furthermore as a demonstration, a nonparametric regression (NPR) model was applied for identifying the location of pollution source, combining air pollution and meteorological measurements. In addition, based on the high time-resolution wind data, local-scale back-trajectories were calculated as an input for receptor-oriented Nonparametric Trajectory Analysis (NTA) model. The combination of the VGP air monitoring system and NTA model identified apparent local sources in urban area. The demonstration was largely successful and operational improvements are clearly suggested to insure better siting and configurations to insure adequate power and air flow.