Regulatory Monitoring Data Research Articles

The value of information in predicting harmful algal blooms

Environmental decision-making is inherently subject to uncertainty. However, decisions are often urgent, and whether to take direct action or invest in collecting additional data beforehand is pervasive. To make this trade-off explicit, the value of information (VoI) theory offers a powerful decision analytic tool to quantify the expected benefit of resolving uncertainty in a decision context. Although it is mainly used in economic contexts, it can be applied to biodiversity conservation and management.In our approach, we evaluate the expected surplus in resolving uncertainty about the occurrence of harmful algal blooms (HABs) in the German North Sea coastal waters and the effect on decision-making. We use an established dynamic foodweb model (NPPZ) with two competing phytoplankton consortia (harmful, non-harmful) and regional monitoring data to analyse the prediction accuracy of different indicators. Our analysis revealed a prediction accuracy of a HAB occurrence of 0.65 % if additional information on zooplankton is included. We then evaluate the effect of reducing uncertainty about these indicators (e.g., through extended monitoring) on management decisions employing a VoI analysis. We find that additional information may lead to an expected welfare gain of up to 2.67 million Euro in our decision context. Our results highlight the significant potential for VoI analysis to enhance decision-making in fishery and ecosystem management and provide insights for future monitoring strategies to mitigate the adverse effects of HABs. This approach contributes valuable methodological insights for optimising management strategies and further emphasises the importance of considering uncertainty in decision-making processes.

A dynamic model and cost-effectiveness on screening coverage and treatment of syphilis included MSM population in the United States

Syphilis is a major sexually transmitted disease, causing a significant public health burden for countries all over the world. Since 2000, there has been a new outbreak of the syphilis epidemic in the United States. Therefore, the prevention and control of syphilis have important research significance. We have established a sex structure and ordinary differential equation model that includes men who have sex with men (MSM). Its epidemiological and biological parameters were obtained by fitting with regional monitoring data from the Centers for Disease Control and Prevention from 1984 to 2014, and the basic reproduction number (R0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}${\\mathcal{R}_{0}}$\\end{document}) of syphilis is 1.3876. Through cost-effectiveness analysis, we have found that the most cost-effective strategies in the cases of sufficient and insufficient funds are conducting syphilis screening for 50% of sexually active susceptible individuals and conducting syphilis screening for 30% of sexually active susceptible individuals while increasing the treatment rate, respectively. Therefore, in the prevention and control strategies of syphilis, measures such as increasing the coverage rate of syphilis screening for susceptible individuals and simultaneously increasing both the screening coverage rate and the treatment rate are valuable control strategy measures for reference.

Coarse Particulate Matter and Markers of Inflammation and Coagulation in the Multi-Ethnic Study of Atherosclerosis (MESA) Population: A Repeat Measures Analysis.

In contrast to fine particles, less is known of the inflammatory and coagulation impacts of coarse particulate matter (, particulate matter with aerodynamic diameter ). Toxicological research suggests that these pathways might be important processes by which impacts health, but there are relatively few epidemiological studies due to a lack of a national monitoring network. We used new spatiotemporal exposure models to examine associations of both 1-y and 1-month average concentrations with markers of inflammation and coagulation. We leveraged data from 7,071 Multi-Ethnic Study of Atherosclerosis and ancillary study participants 45-84 y of age who had repeated plasma measures of inflammatory and coagulation biomarkers. We estimated at participant addresses 1 y and 1 month before each of up to four exams (2000-2012) using spatiotemporal models that incorporated satellite, regulatory monitoring, and local geographic data and accounted for spatial correlation. We used random effects models to estimate associations with interleukin-6 (IL-6), C-reactive protein (CRP), fibrinogen, and D-dimer, controlling for potential confounders. Increases in were not associated with greater levels of inflammation or coagulation. A increase in annual average was associated with a 2.5% decrease in CRP [95% confidence interval (CI): , 0.6]. We saw no association between annual average and the other markers (IL-6: , 95% CI: , 1.2; fibrinogen: , 95% CI: , 0.3; D-dimer: , 95% CI: , 2.4). Associations consistently showed that a increase in 1-month average was associated with reduced inflammation and coagulation, though none were distinguishable from no association (IL-6: , 95% CI: , 0.5; CRP: , 95% CI: , 0.4; fibrinogen: , 95% CI: , 0.1; D-dimer: , 95% CI: , 0.3). We found no evidence that is associated with higher inflammation or coagulation levels. More research is needed to determine whether the inflammation and coagulation pathways are as important in explaining observed health impacts in humans as they have been shown to be in toxicology studies or whether might impact human health through alternative biological mechanisms. https://doi.org/10.1289/EHP12972.

Generating high spatial resolution exposure estimates from sparse regulatory monitoring data

Random Forest algorithms have extensively been used to estimate ambient air pollutant concentrations. However, the accuracy of model-predicted estimates can suffer from extrapolation problems associated with limited measurement data to train the machine learning algorithms. In this study, we developed and evaluated two approaches, incorporating low-cost sensor data, that enhanced the extrapolating ability of random-forest models in areas with sparse monitoring data. Rochester, NY is the area of a pregnancy-cohort study. Daily PM2.5 concentrations from the NAMS/SLAMS sites were obtained and used as the response variable in the model, with satellite data, meteorological, and land-use variables included as predictors. To improve the base random-forest models, we used PM2.5 measurements from a pre-existing low-cost sensors network, and then conducted a two-step backward selection to gradually eliminate variables with potential emission heterogeneity from the base models. We then introduced the regression-enhanced random forest method into the model development. Finally, contemporaneous urinary 1-hydroxypyrene was used to evaluate the PM2.5 predictions generated from the two approaches. The two-step approach increased the average external validation R2 from 0.49 to 0.65, and decreased the RMSE from 3.56 μg/m3 to 2.96 μg/m3. For the regression-enhanced random forest models, the average R2 of the external validation was 0.54, and the RMSE was 3.40 μg/m3. We also observed significant and comparable relationships between urinary 1-hydroxypyrene levels and PM2.5 predictions from both improved models. This PM2.5 model estimation strategy could improve the extrapolating ability of random forest models in areas with sparse monitoring data.

The impact of COVID-19 public health restrictions on particulate matter pollution measured by a validated low-cost sensor network in Oxford, UK

Emergency responses to the COVID-19 pandemic led to major changes in travel behaviours and economic activities with arising impacts upon urban air quality. To date, these air quality changes associated with lockdown measures have typically been assessed using limited city-level regulatory monitoring data, however, low-cost air quality sensors provide capabilities to assess changes across multiple locations at higher spatial-temporal resolution, thereby generating insights relevant for future air quality interventions. The aim of this study was to utilise high-spatial resolution air quality information utilising data arising from a validated (using a random forest field calibration) network of 15 low-cost air quality sensors within Oxford, UK to monitor the impacts of multiple COVID-19 public heath restrictions upon particulate matter concentrations (PM10, PM2.5) from January 2020 to September 2021. Measurements of PM10 and PM2.5 particle size fractions both within and between site locations are compared to a pre-pandemic related public health restrictions baseline. While average peak concentrations of PM10 and PM2.5 were reduced by 9–10 μg/m3 below typical peak levels experienced in recent years, mean daily PM10 and PM2.5 concentrations were only ∼1 μg/m3 lower and there was marked temporal (as restrictions were added and removed) and spatial variability (across the 15-sensor network) in these observations. Across the 15-sensor network we observed a small local impact from traffic related emission sources upon particle concentrations near traffic-oriented sensors with higher average and peak concentrations as well as greater dynamic range, compared to more intermediate and background orientated sensor locations. The greater dynamic range in concentrations is indicative of exposure to more variable emission sources, such as road transport emissions. Our findings highlight the great potential for low-cost sensor technology to identify highly localised changes in pollutant concentrations as a consequence of changes in behaviour (in this case influenced by COVID-19 restrictions), generating insights into non-traffic contributions to PM emissions in this setting. It is evident that additional non-traffic related measures would be required in Oxford to reduce the PM10 and PM2.5 levels to within WHO health-based guidelines and to achieve compliance with PM2.5 targets developed under the Environment Act 2021.

Prediction of Surface Subsidence Based on PSO-BP Neural Network

The traditional settlement monitoring has the problems of small monitoring range and low adaptability of settlement prediction model; Based on SBAS-InSAR technology and PSO-BP neural network algorithm, this paper constructs a PSO-BP settlement prediction model based on Sentinel-1A data. Take Jinan(E116°8’, N36°8’)the regional monitoring data is a data sample set. The algorithm proposed in this paper is compared with typical algorithms such as SVM. The results show that the MAE of 30 points predicted by this algorithm is 0.17, which is better than six typical algorithms such as SVM. This algorithm can be used as an effective means to provide early warning of surface subsidence.

Regional monitoring of biomass burning using passive air sampling technique reveals the importance of MODIS unresolved fires

Field-based sampling can provide more accurate evaluation than MODIS in regional biomass burning (BB) emissions given the limitations of MODIS on unresolved fires. Polyurethane foam-based passive air samplers (PUF-PASs) are a promising tool for collecting atmospheric monosaccharides. Here, we deployed PUF-PASs to monitor monosaccharides and other BB-related biomarkers and presented a dataset of 31 atmospheric BB-related biomarkers in the Indo-China Peninsula (ICP) and Southwest China. The peak concentrations of monosaccharides in the ICP occurred before monsoon season. The highest concentrations were in the eastern Mekong plain, while the lowest were along the eastern coast. BB-related biomarkers displayed elevated concentrations after April, particularly in the monsoon season; however, fewer active fires were recorded by MODIS. This revealed the importance of MODIS unresolved fires (e.g., indoor biofuel combustion, small-scale BB incidents, and charcoal fires) to the regional atmosphere. The PAS derived levoglucosan concentrations indicated that, with the inclusion of MODIS unresolved fires, the estimated top-down emissions of PM (4194–4974 Gg/yr), OC (1234–1719 Gg/yr) and EC (52–384 Gg/yr) would be higher than previous bottom-up estimations in the ICP. Future studies on these MODIS unresolved fires and regional monitoring data of BB are vital for improving the modeling of regional BB emissions.

Nitrogen dioxide increases the risk of disease progression in idiopathic pulmonary fibrosis.

Air pollution affects clinical course and prognosis of idiopathic pulmonary fibrosis (IPF). However, the effect of individual exposure to air pollutants on disease progression is unclear. We aimed to identify the effect of individual exposure to nitrogen dioxide (NO2 ) and particulate matter (aerodynamic diameter ≤ 10μm [PM10 ]) on disease progression in patients with IPF. The serial lung function data of 946 IPF patients (mean age: 65.4 years, male: 80.9%) were analysed. Individual-level long-term exposures to NO2 and PM10 at the residential addresses of patients were estimated using a national-scale exposure prediction model, constructed based on air quality regulatory monitoring data. Progression was defined as a relative decline (≥10%) in forced vital capacity. Individual- and area-level covariates were adjusted in the primary analysis model. Overall, 547 patients (57.8%) experienced progression during a median follow-up of 1.0year (interquartile range: 0.4-2.6 years). In the primary model, a 10-ppb increase in NO2 concentration was associated with a 10.5% increase in the risk of progression (hazard ratio [HR]=1.105; 95% CI=1.000-1.219) in patients with IPF. There was also an increasing trend of progression in patients with IPF according to the second to fourth quartiles of NO2 (Q2 [HR=1.299; 95% CI=0.972-1.735], Q3 [1.409; 1.001-1.984], Q4 [1.598; 1.106-2.310]) compared to the first quartile. We found no association between PM10 and progression in IPF patients. Our data suggest that increased individual exposure to NO2 can increase the risk of progression in patients with IPF.

Within-city variation in ambient carbon monoxide concentrations: Leveraging low-cost monitors in a spatiotemporal modeling framework

Exposure to ambient carbon monoxide (CO) may be associated with cardiovascular disease outcomes based on human and animal experimental evidence, but current epidemiologic research is limited. The number and distribution of ground-level regulatory agency monitors are insufficient to characterize potential fine-scale variation of CO. This study aimed to develop a high-resolution ambient CO prediction model at the daily scale based on both regulatory agency monitoring data and measurements from calibrated low-cost gas monitors in Baltimore, Maryland. We also evaluated the contribution of three novel parameters to model performance, including high-resolution meteorological data, satellite remote sensing data, and co-pollutant concentrations (PM2.5, NO2, and NOx). The CO model had spatial cross-validation (CV) R² (RMSE) of 0.70 (0.02 parts per million, ppm) and temporal CV R² (RMSE) of 0.61 (0.04 ppm). The predictions revealed spatially resolved CO hotspots associated with population, traffic, and other non-road emission sources (e.g., railroads and airport), as well as sharp concentration decreases within short distances from primary roads. The three novel parameters did not substantially improve model performance, suggesting that our spatiotemporal modeling framework based on geographic features was reliable and robust. As low-cost monitors become increasingly available, this approach to CO concentration modeling can be generalized to resource-restricted environments to facilitate comprehensive epidemiologic research.

Within-City Variation in Ambient Carbon Monoxide Concentrations: Leveraging Low-Cost Monitors in a Spatiotemporal Modeling Framework.

Background:Based on human and animal experimental studies, exposure to ambient carbon monoxide (CO) may be associated with cardiovascular disease outcomes, but epidemiological evidence of this link is limited. The number and distribution of ground-level regulatory agency monitors are insufficient to characterize fine-scale variations in CO concentrations.Objectives:To develop a daily, high-resolution ambient CO exposure prediction model at the city scale.Methods:We developed a CO prediction model in Baltimore, Maryland, based on a spatiotemporal statistical algorithm with regulatory agency monitoring data and measurements from calibrated low-cost gas monitors. We also evaluated the contribution of three novel parameters to model performance: high-resolution meteorological data, satellite remote sensing data, and copollutant (, , and ) concentrations.Results:The CO model had spatial cross-validation (CV) and root-mean-square error (RMSE) of (ppm), respectively; the model had temporal CV and RMSE of 0.61 and , respectively. The predictions revealed spatially resolved CO hot spots associated with population, traffic, and other nonroad emission sources (e.g., railroads and airport), as well as sharp concentration decreases within short distances from primary roads.Discussion:The three novel parameters did not substantially improve model performance, suggesting that, on its own, our spatiotemporal modeling framework based on geographic features was reliable and robust. As low-cost air monitors become increasingly available, this approach to CO concentration modeling can be generalized to resource-restricted environments to facilitate comprehensive epidemiological research. https://doi.org/10.1289/EHP10889

Examining the factors influencing mathematics academic achievement in mainland China: A multilevel analysis

ABSTRACT This study used large-scale regional monitoring data of eighth-grade mathematics students at the compulsory education stage from various areas of mainland China. It extracted a total of 156,661 students and 4,676 junior high school mathematics teachers from 146 districts and counties located in six regions (provinces or cities). The study analysed academic achievement in mathematics in these areas and established a hierarchical linear model to explore the factors affecting academic achievement at different levels. The results are as follows: (1) approximately 94% of eighth-grade students reached the level C academic benchmark - students in East China had the highest compliance rate with this level, followed by those in North, South, and Central China; (2) girls, non-leftover students, and children without siblings performed better, and urban students performed significantly better than county and rural students; (3) approximately 34% of students’ mathematics academic performance came from inter-school variability - regional background had a greater impact on mathematics than did teaching factors, while urban and rural background had the least impact. In contrast, the influence of individual characteristic variables was higher than that of student background variables, including a greater positive effect of self-efficacy and a greater negative effect of mathematics anxiety.

Assessing the Extent of Environmental Risks From Nickel in European Freshwaters: A Critical Reflection of the European Commission's Current Approach.

Nickel (Ni) has a been a Priority Substance under the European Water Framework Directive since 2008. As such it is deemed to present an European Union‐wide risk to surface waters. Since 2013, the Ni Environmental Quality Standard (EQS) has been bioavailability‐based, and new European Guidance supports accounting for bioavailability in assessing Ni compliance with the EQS. The European Commission has developed an approach to determine whether Priority Substances present a sufficient European Union‐wide risk to justify an ongoing statutory monitoring programme, effectively to deselect a substance. This is a key step to ensure that finite monitoring resources are targeted at delivering environmental benefit, when there is an ever‐growing burden of determinands to measure for all regulators. When the European Commission performed this exercise for Ni without accounting for bioavailability, they concluded that Ni should not be deselected, and Ni is an European Union‐wide risk. Performing this same exercise with the same methodology, using regulatory monitoring data for over 300 000 samples, from more than 19 000 sites across Europe, and accounting for bioavailability, as detailed in the Directive, >99% of sites comply with the Ni EQS. Nickel shows very low risks for all of the criteria identified by the European Commission that need to be met for deselection. Accounting for bioavailability is key in the assessment of Ni risks in surface waters to deliver ecologically relevant outcomes. Environ Toxicol Chem 2022;41:1604–1612. © 2022 NiPERA. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Development of a novel method for groundwater flow/land subsidence model calibration using regional monitoring data on land surface displacement including regional crustal deformation

Development of a novel method for groundwater flow/land subsidence model calibration using regional monitoring data on land surface displacement including regional crustal deformation

Characteristics of atmospheric air pollution by fine particles based on regional monitoring data

Introduction. Air pollution with particulate matter (PM) is a serious global problem. In the Russian Federation, regular field measurements of PMs in the ambient air are carried out only in a few cities, and the data, as a rule, are not systematized. Aim of the study: long-term analysis of the data set on concentrations of fine particles in the ambient air of the city of Kazan. Material and methods. Long-term analysis of ambient air pollution by fine particles in the city of Kazan for the period from 2016 to 2020 has been carried out. To study the effect of separate factors (year, measurement time during the day, climatic conditions, the presence of other pollutants) on the levels of PM10 and PM2.5, regression analysis was applied based on the method of mixed models. To characterize the elemental composition of the PM2.5 fraction, sampling of atmospheric air on PVC filters was carried out by use of 100 NR impactor (TSI, USA). The step function and MPPD model were applied to calculate the number of particles and the mass of the deposited fraction of fine particulate matter in different regions of the human respiratory tract. Results. The PM10 concentrations remained stable over a 5-year period, while the PM2.5 concentrations decreased. At the same time, an increase in the maximum annual concentrations of both fractions was observed. The concentrations of PM10 and PM2.5 significantly depended on climatic conditions. The presence of nitrogen oxides and organic carbon in the ambient air was significantly associated with higher concentrations of PM10 and PM2.5. The elemental composition of PM2.5 fraction was represented mainly by carbon (C) (from 86.16% to 93.45%). Mathematical modeling has shown that PM10 is mainly deposited in the upper respiratory tract, and their presence in the tracheobronchial and alveolar zones is insignificant. PM2.5 particles reach the lower respiratory tract and alveolar area. Conclusion. A statistically significant upward long-term trend in the maximum annual ambient concentrations for both fractions of fine particles can increase health risks. Secondary pollutants (nitrogen oxides, organic carbon) are important factors for the formation of secondary particles in the ambient air. The results obtained indicate that when assessing the risks to public health, it is necessary not only to use the concentrations of fine particles in ambient air, but also to consider the degree of deposition of separate fractions in different parts of the human respiratory tract, considering the alleged pathogenesis and priority target cells characteristic of individual diseases.

A Quantitative Soil-Geomorphic Framework for Developing and Mapping Ecological Site Groups

Land management decisions need context about how landscapes will respond to different circumstances or actions. As ecologists’ understanding of nonlinear ecological dynamics has evolved into state-and-transition models (STMs), they have put more emphasis on defining and mapping the soil, geomorphological, and climate parameters that mediate these dynamics. The US Department of Agriculture Natural Resources Conservation Service ecological site descriptions (ESDs) have become the foremost system in classifying lands into ecological units based on STMs. However, an exhaustive inventory of ESDs has proved challenging to complete in the United States, and there have been questions about the consistency of detail in areas completed and the ability to objectively support some assertions made in existing ESDs. To address these issues, this study examines ESDs in the diverse Upper Colorado River region, where ESDs are only partially complete, to look at quantitative approaches to generalizing ecological site concepts based on unifying underlying soil, geomorphology, and climate patterns. Using existing ESDs and vegetation monitoring plot data, results show that a simple hierarchical soil geomorphic unit (SGU) framework based on topographic mediation of moisture, soil salinity, soil depth, slope, rock content, and soil texture can represent much of the ecological dynamics cataloged in ESDs. Analyses of reference plant production data, ecological state attribution, and regional monitoring data show that the new SGUs represent more variation than common climate parameters. This study also included predictively mapping SGUs at 30-m resolution (Kappa of 0.53, 74% agreement with top two predictions in validation). An optimized combination of SGUs with climate zones derived from an aridity index and maximum temperature of the hottest month resulted in an ecological site group framework that condensed over 826 unique ecological site records at various stages of completeness in the regional soil survey down to 35 intuitive and mappable ecological site groups.

Reanalysis of the association between reduction in long-term PM2.5 concentrations and improved life expectancy

BackgroundMuch of the current evidence of associations between long-term PM2.5 and health outcomes relies on national or regional analyses using exposures derived directly from regulatory monitoring data. These findings could be affected by limited spatial coverage of monitoring data, particularly for time periods before spatially extensive monitoring began in the late 1990s. For instance, Pope et al. (2009) showed that between 1980 and 2000 a 10 μg/m3 reduction in PM2.5 was associated with an average 0.61 year (standard error (SE) = 0.20) longer life expectancy. That analysis used 1979–1983 averages of PM2.5 across 51 U.S. Metropolitan Statistical Areas (MSAs) computed from about 130 monitoring sites. Our reanalysis re-examines this association using modeled PM2.5 in order to assess population- or spatially-representative exposure. We hypothesized that modeled PM2.5 with finer spatial resolution provides more accurate health effect estimates compared to limited monitoring data.MethodsWe used the same data for life expectancy and confounders, as well as the same analysis models, and investigated the same 211 continental U.S. counties, as Pope et al. (2009). For modeled PM2.5, we relied on a previously-developed point prediction model based on regulatory monitoring data for 1999–2015 and back-extrapolation to 1979. Using this model, we predicted annual average concentrations at centroids of all 72,271 census tracts and 12,501 25-km national grid cells covering the contiguous U.S., to represent population and space, respectively. We averaged these predictions to the county for the two time periods (1979–1983 and 1999–2000), whereas the original analysis used MSA averages given limited monitoring data. Finally, we estimated regression coefficients for PM2.5 reduction on life expectancy improvement over the two periods, adjusting for area-level confounders.ResultsA 10 μg/m3 decrease in modeled PM2.5 based on census tract and national grid predictions was associated with 0.69 (standard error (SE) = 0.31) and 0.81 (0.29) -year increases in life expectancy. These estimates are higher than the estimate of Pope et al. (2009); they also have larger SEs likely because of smaller variability in exposure predictions, a standard property of regression. Two sets of effect estimates, however, had overlapping confidence intervals.ConclusionsOur approach for estimating population- and spatially-representative PM2.5 concentrations based on census tract and national grid predictions, respectively, provided generally consistent findings to the original findings using limited monitoring data. This finding lends additional support to the evidence that reduced fine particulate matter contributes to extended life expectancy.

Transition to a New Model of Russian Governors’ Appointments as a Reflection of Regime Transformation

Abstract An array of qualitative and quantitative information on 2016–2020 Russian governors’ replacements is used to analyze decision-making and the patterns of governors’ appointments as well as their evolution in conjunction with regime transformation and regional variation. Regression analysis is used to establish what factors influence replacements, based on regional monitoring data from the Committee of Civic Initiatives. Ethnic republics in the North Caucasus are taken as a special case.

Childhood traffic-related air pollution and adverse changes in subclinical atherosclerosis measures from childhood to adulthood

BackgroundChronic exposure to air pollutants is associated with increased risk of cardiovascular disease (CVD) among adults. However, little is known about how air pollution may affect the development of subclinical atherosclerosis in younger populations. Carotid artery intima-media thickness (CIMT) is a measure of subclinical atherosclerosis that provides insight into early CVD pathogenesis.MethodsIn a pilot study of 70 participants from the Southern California Children’s Health Study, we investigated CIMT progression from childhood to adulthood. Using carotid artery ultrasound images obtained at age 10 and follow-up images at age 21–22, we examined associations between childhood ambient and traffic-related air pollutants with changes in CIMT over time and attained adult CIMT using linear mixed-effects models adjusted for potential confounders.Average residential childhood exposures (i.e., birth to time of measurement at 10–11 years) were assigned for regional, ambient pollutants (ozone, nitrogen dioxide, particulate matter, interpolated from regulatory air monitoring data) and traffic-related nitrogen oxides (NOx) by road class (modeled using the CALINE4 line source dispersion model). Traffic density was calculated within a 300-m residential buffer.ResultsFor each 1 standard deviation (SD) increase in childhood traffic-related total NOx exposure, we observed greater yearly rate of change in CIMT from childhood to adulthood (β: 2.17 μm/yr, 95% CI: 0.78–3.56). Increases in annual rate of CIMT change from childhood to adulthood also were observed with freeway NOx exposure (β: 2.24 μm/yr, 95% CI: 0.84–3.63) and traffic density (β: 2.11 μm/yr, 95% CI: 0.79–3.43). Traffic exposures were also related to increases in attained CIMT in early adulthood. No associations of CIMT change or attained level were observed with ambient pollutants.ConclusionsOverall, we observed adverse changes in CIMT over time in relation to childhood traffic-related NOx exposure and traffic density in our study population. While these results must be cautiously interpreted given the limited sample size, the observed associations of traffic measures with CIMT suggest a need for future studies to more fully explore this relationship.

Development of a mobile platform for monitoring gaseous, particulate, and greenhouse gas (GHG) pollutants.

The Michigan Pollution Assessment Laboratory (MPAL) is a mobile air quality monitoring platform designed to measure conventional, toxic, and greenhouse gas (GHG) air pollutants. The spatially and temporally resolved data collected can be used for multiple purposes, such as mapping spatial patterns and identifying peaks. The truck-based platform includes instrumentation for 11 gaseous pollutants and for particulate matter (PM), size distribution (7nm to 20μm), PM10, black and brown carbon, and trace metals. MPAL is equipped with meteorological instruments, a high-accuracy GPS, forward and reverse cameras, and a data logging and display system. We selected commercially available instrumentation based on sensitivity, response time, and robustness. The vehicle's power system allows ~ 6.5h of continuous operation with all instruments operating. This article details the design, construction, and evaluation of MPAL and summarizes data collected in its first year (March 2019 to March 2020) of operation. We completed a series of runs on 84days in Detroit, Michigan, an area with a diverse set of traffic, industrial, and commercial emission sources, and collected 265,816 1-s observations (excluding collocations, zero checks, and other quality assurance measurements). Using data from these runs as well as special tests, we present results of performance evaluations that examined the response time, PM losses, and wind measurements and compare results to stationary regulatory monitoring data. We highlight key issues and provide practical solutions to help evaluate and resolve these issues and share many lessons learned in developing and using a mobile platform.

New insights into the association of modeled long-term PM2.5 with life expectancy compared to measured PM2.5

Much of the current evidence of associations between long-term PM2.5 and health outcomes relies on national or regional analyses using exposures derived directly from regulatory monitoring data. These findings could be affected by limited spatial coverage of monitoring data, particularly for time periods before spatially extensive monitoring began in late 1990s. For instance, Pope et al.(2009) showed that between 1980 and 2000 a 10 µg/m3 reduction in PM2.5 was associated with an average 0.61 year (standard error (SE)=0.20) longer life expectancy. That analysis used 1979-1983 averages of PM2.5 across 51 U.S. Metropolitan Statistical Areas (MSAs) computed from about 100 monitoring sites. Our reanalysis re-examined this association using modeled PM2.5 that intended to assess population- or geographically-representative exposure.We used the same life expectancy data, and investigated the same 211 continental U.S. counties, as Pope et al.(2009). For modelled PM2.5, using a previously-developed point prediction model based on regulatory monitoring data for 1990-2015 and back-extrapolation. We predicted annual average concentrations at centroids of 70,000 census tracts (CT) and 12,501 25-km national grid cells (NG), to represent population and geography, respectively. We averaged these predictions to the MSA for the two time periods (1979-1983 and 1999-2000) that correspond to the original analysis. Finally, we estimated regression coefficients for PM2.5 reduction on life expectancy improvement over the two periods, adjusting for area-level confounders.A 10 µg/m3 decrease in modeled PM2.5 based on CT and NG predictions was associated with average 0.67 (SE= 0.35) and 0.97 (0.38) year increases in life expectancy, higher than the estimate of Pope et al.(2009). These estimates have larger SEs likely because of smaller variability in predictions. All effect estimates had overlapping confidence intervals.Our approach to estimating population- or geographically-representative PM2.5 concentrations lends additional support to the evidence that reduced fine particulate matter contributes to extended life expectancy.