Special Issue "Air Quality Monitoring and Forecasting"

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Air Quality".

Deadline for manuscript submissions: closed (30 June 2017)

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Guest Editor
Dr. Pius Lee

1. Adjunct Professor, CSISS, George Mason University, 4400 University Dr, Fairfax, VA 22030, USA
2. NOAA/Air Resources Lab National Air Quality Forecasting Capability Project Leader, NCWCP, College Park, MD 20740, USA
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Interests: atmospheric, geophysical and human health interdisciplinary research
Guest Editor
Dr. Rick Saylor

NOAA Air Resources Laboratory, Atmospheric Turbulence and Diffusion Division 456 South Illinois Avenue, Oak Ridge, TN 37830, USA
Website | E-Mail
Interests: atmospheric chemistry and atmosphere-surface exchange
Guest Editor
Mr. Jeff McQueen

NOAA/NWS/NCEP/EMC, NOAA Center for Weather and Climate Prediction 5830 University Research Court College Park, MD 20740, USA
Website | E-Mail
Interests: meteorology and boundary layer physics

Special Issue Information

Dear Colleagues,

Atmosphere dedicates this special issue to air quality monitoring and forecasting. Forecasting is a vital tool for local health and air quality managers to make informed short-term decisions on remedial and mitigation measures to reduce exposure risks for their residents. Forecasting tools enable them to issue air quality advisories to curb pollution by limiting vehicular traffic by encouraging car-pooling and offering free public transportation. Air quality monitoring from the perspective of air managers serves a dual purpose of evaluating the skill of their forecasting tools and deriving long-term trends of major air pollutants that impact their constituents. Epidemiologists also use long term monitored data to understand air pollution related diseases and mortality rates to support public health policy decisions. This Special Issue reinforces the importance of these tools by leveraging their collective strengths.

Public health is under a constant threat by air pollution across the world in various degrees and manifestations. In some countries with rapid economic growth the abrupt increased occurrences of poor air quality over cities and their downwind regions are attracting worldwide attention. The adverse health effects suffered by the public are reflected in billions of dollars in lost productivity, hospital admissions due to contraction and exacerbation of respiratory, asthmatic and cardiovascular diseases, and increases in mortality rates. This is especially true in rapidly developing countries. On the other hand, many cities in developed countries are seeing changes in their atmospheric chemical regimes from nitrogen oxide (NOx) saturated regimes towards NOx limiting regimes. Furthermore, ozone and ozone precursors transported from areas upwind become important source of “background ozone” as local generation of ozone plays a lesser role due to reduced NOx emissions in the developed countries. It is now clear that air pollution is a global problem and that air quality monitoring, forecasting and mitigation should be a local effort conducted in concert with global partners.

In light of the global, trans-continental, and rapidly changing chemical and emission characteristics of the world's air quality, we invite you to contribute articles to this special issue by reporting on observation and modeling and policy-relevant studies about air quality monitoring and forecasting for regional and/or global applications. Solicited contributions include but are not limited to: atmospheric chemical studies through observation and/or forecast modeling of pollution emission, transport, transformation, removal, fate of air pollutants and their health impact on humans. Articles on chemical analysis and operational air quality forecasting performance and evaluation are also encouraged.

Dr. Pius Lee
Dr. Rick Saylor
Mr. Jeff McQueen
Guest Editors

Manuscript Submission Information

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Keywords

  • Air Quality and atmospheric composition modeling
  • Atmospheric chemical observation and monitoring
  • Air quality forecasting
  • Air pollutant related epidemiology and exposure studies
  • Climate impact on air quality forecasting

Published Papers (13 papers)

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Editorial

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Open AccessEditorial Air Quality Monitoring and Forecasting
Atmosphere 2018, 9(3), 89; https://doi.org/10.3390/atmos9030089
Received: 27 February 2018 / Revised: 28 February 2018 / Accepted: 28 February 2018 / Published: 1 March 2018
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Abstract
Air quality forecasting is a vital tool for local health and air managers to make informed decisions on mitigation measures to reduce public exposure risk.[...] Full article
(This article belongs to the Special Issue Air Quality Monitoring and Forecasting) Printed Edition available

Research

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Open AccessArticle Evaluation of Analysis by Cross-Validation. Part I: Using Verification Metrics
Atmosphere 2018, 9(3), 86; https://doi.org/10.3390/atmos9030086
Received: 5 September 2017 / Revised: 20 February 2018 / Accepted: 24 February 2018 / Published: 27 February 2018
Cited by 1 | PDF Full-text (2893 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
We examine how passive and active observations are useful to evaluate an air quality analysis. By leaving out observations from the analysis, we form passive observations, and the observations used in the analysis are called active observations. We evaluated the surface air quality
[...] Read more.
We examine how passive and active observations are useful to evaluate an air quality analysis. By leaving out observations from the analysis, we form passive observations, and the observations used in the analysis are called active observations. We evaluated the surface air quality analysis of O3 and PM2.5 against passive and active observations using standard model verification metrics such as bias, fractional bias, fraction of correct within a factor of 2, correlation and variance. The results show that verification of analyses against active observations always give an overestimation of the correlation and an underestimation of the variance. Evaluation against passive or any independent observations display a minimum of variance and maximum of correlation as we vary the observation weight, thus providing a mean to obtain the optimal observation weight. For the time and dates considered, the correlation between (independent) observations and the model is 0.55 for O3 and 0.3 for PM2.5 and for the analysis, with optimal observation weight, increases to 0.74 for O3 and 0.54 for PM2.5. We show that bias can be a misleading measure of evaluation and recommend the use of a fractional bias such as the modified normalized mean bias (MNMB). An evaluation of the model bias and variance as a function of model values also show a clear linear dependence with the model values for both O3 and PM2.5. Full article
(This article belongs to the Special Issue Air Quality Monitoring and Forecasting) Printed Edition available
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Open AccessArticle Evaluation of Analysis by Cross-Validation, Part II: Diagnostic and Optimization of Analysis Error Covariance
Atmosphere 2018, 9(2), 70; https://doi.org/10.3390/atmos9020070
Received: 7 November 2017 / Revised: 19 January 2018 / Accepted: 13 February 2018 / Published: 15 February 2018
Cited by 1 | PDF Full-text (1486 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
We present a general theory of estimation of analysis error covariances based on cross-validation as well as a geometric interpretation of the method. In particular, we use the variance of passive observation-minus-analysis residuals and show that the true analysis error variance can be
[...] Read more.
We present a general theory of estimation of analysis error covariances based on cross-validation as well as a geometric interpretation of the method. In particular, we use the variance of passive observation-minus-analysis residuals and show that the true analysis error variance can be estimated, without relying on the optimality assumption. This approach is used to obtain near optimal analyses that are then used to evaluate the air quality analysis error using several different methods at active and passive observation sites. We compare the estimates according to the method of Hollingsworth-Lönnberg, Desroziers et al., a new diagnostic we developed, and the perceived analysis error computed from the analysis scheme, to conclude that, as long as the analysis is near optimal, all estimates agree within a certain error margin. Full article
(This article belongs to the Special Issue Air Quality Monitoring and Forecasting) Printed Edition available
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Open AccessArticle Overview of the Model and Observation Evaluation Toolkit (MONET) Version 1.0 for Evaluating Atmospheric Transport Models
Atmosphere 2017, 8(11), 210; https://doi.org/10.3390/atmos8110210
Received: 16 June 2017 / Revised: 19 October 2017 / Accepted: 21 October 2017 / Published: 31 October 2017
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Abstract
This paper describes the development and initial applications of the Model and Observation Evaluation Tool (MONET) v1.0. MONET was developed to evaluate the Community Multiscale Air Quality Model (CMAQ) for the NOAA National Air Quality Forecast Capability (NAQFC) modeling system. MONET is designed
[...] Read more.
This paper describes the development and initial applications of the Model and Observation Evaluation Tool (MONET) v1.0. MONET was developed to evaluate the Community Multiscale Air Quality Model (CMAQ) for the NOAA National Air Quality Forecast Capability (NAQFC) modeling system. MONET is designed to be a modularized Python package for (1) pairing model output to observational data in space and time; (2) leveraging the pandas Python package for easy searching and grouping; and (3) analyzing and visualizing data. This process introduces a convenient method for evaluating model output. MONET processes data that is easily searchable and that can be grouped using meta-data found within the observational datasets. Common statistical metrics (e.g., bias, correlation, and skill scores), plotting routines such as scatter plots, timeseries, spatial plots, and more are included in the package. MONET is well modularized and can add further observational datasets and different models. Full article
(This article belongs to the Special Issue Air Quality Monitoring and Forecasting) Printed Edition available
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Open AccessArticle Characterization of Particulate Matter (PM2.5 and PM10) Relating to a Coal Power Plant in the Boroughs of Springdale and Cheswick, PA
Atmosphere 2017, 8(10), 186; https://doi.org/10.3390/atmos8100186
Received: 7 August 2017 / Revised: 24 August 2017 / Accepted: 18 September 2017 / Published: 23 September 2017
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Abstract
Ambient concentrations of both fine particulate matter (PM2.5) and particulate matter with an aerodynamic diameter less than 10 micron (PM10) were measured from 10 June 2015 to 13 July 2015 at three locations surrounding the Cheswick Power Plant, which
[...] Read more.
Ambient concentrations of both fine particulate matter (PM2.5) and particulate matter with an aerodynamic diameter less than 10 micron (PM10) were measured from 10 June 2015 to 13 July 2015 at three locations surrounding the Cheswick Power Plant, which is located between the boroughs of Springdale and Cheswick, Pennsylvania. The average concentrations of PM10 observed during the periods were 20.5 ± 10.2 μg m−3 (Station 1), 16.1 ± 4.9 μg m−3 (Station 2) and 16.5 ± 7.1 μg m−3 (Station 3). The average concentrations of PM2.5 observed at the stations were 9.1 ± 5.1 μg m−3 (Station 1), 0.2 ± 0.4 μg m−3 (Station 2) and 11.6 ± 4.8 μg m−3 (Station 3). In addition, concentrations of PM2.5 measured by four Pennsylvania Department of Environmental Protection air quality monitors (all within a radius of 40 miles) were also analyzed. The observed average concentrations at these sites were 12.7 ± 6.9 μg m−3 (Beaver Falls), 11.2 ± 4.7 μg m−3 (Florence), 12.2 ± 5.3 μg m−3 (Greensburg) and 12.2 ± 5.5 μg m−3 (Washington). Elemental analysis for samples (blank – corrected) revealed the presence of metals that are present in coal (i.e., antimony, arsenic, beryllium, cadmium, chromium, cobalt, lead, manganese, mercury, nickel and selenium). Full article
(This article belongs to the Special Issue Air Quality Monitoring and Forecasting) Printed Edition available
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Open AccessArticle Multi-Year (2013–2016) PM2.5 Wildfire Pollution Exposure over North America as Determined from Operational Air Quality Forecasts
Atmosphere 2017, 8(9), 179; https://doi.org/10.3390/atmos8090179
Received: 30 June 2017 / Revised: 8 September 2017 / Accepted: 14 September 2017 / Published: 19 September 2017
Cited by 2 | PDF Full-text (8873 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
FireWork is an on-line, one-way coupled meteorology–chemistry model based on near-real-time wildfire emissions. It was developed by Environment and Climate Change Canada to deliver operational real-time forecasts of biomass-burning pollutants, in particular fine particulate matter (PM2.5), over North America. Such forecasts
[...] Read more.
FireWork is an on-line, one-way coupled meteorology–chemistry model based on near-real-time wildfire emissions. It was developed by Environment and Climate Change Canada to deliver operational real-time forecasts of biomass-burning pollutants, in particular fine particulate matter (PM2.5), over North America. Such forecasts provide guidance for early air quality alerts that could reduce air pollution exposure and protect human health. A multi-year (2013–2016) analysis of FireWork forecasts over a five-month period (May to September) was conducted. This work used an archive of FireWork outputs to quantify wildfire contributions to total PM2.5 surface concentrations across North America. Different concentration thresholds (0.2 to 28 µg/m3) and averaging periods (24 h to five months) were considered. Analysis suggested that, on average over the fire season, 76% of Canadians and 69% of Americans were affected by seasonal wildfire-related PM2.5 concentrations above 0.2 µg/m3. These effects were particularly pronounced in July and August. Futhermore, the analysis showed that fire emissions contributed more than 1 µg/m3 of daily average PM2.5 concentrations on more than 30% of days in the western USA and northwestern Canada during the fire season. Full article
(This article belongs to the Special Issue Air Quality Monitoring and Forecasting) Printed Edition available
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Open AccessArticle Comparing CMAQ Forecasts with a Neural Network Forecast Model for PM2.5 in New York
Atmosphere 2017, 8(9), 161; https://doi.org/10.3390/atmos8090161
Received: 30 June 2017 / Revised: 22 August 2017 / Accepted: 26 August 2017 / Published: 29 August 2017
Cited by 1 | PDF Full-text (2280 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Human health is strongly affected by the concentration of fine particulate matter (PM2.5). The need to forecast unhealthy conditions has driven the development of Chemical Transport Models such as Community Multi-Scale Air Quality (CMAQ). These models attempt to simulate the complex
[...] Read more.
Human health is strongly affected by the concentration of fine particulate matter (PM2.5). The need to forecast unhealthy conditions has driven the development of Chemical Transport Models such as Community Multi-Scale Air Quality (CMAQ). These models attempt to simulate the complex dynamics of chemical transport by combined meteorology, emission inventories (EI’s), and gas/particle chemistry and dynamics. Ultimately, the goal is to establish useful forecasts that could provide vulnerable members of the population with warnings. In the simplest utilization, any forecast should focus on next day pollution levels, and should be provided by the end of the business day (5 p.m. local). This paper explores the potential of different approaches in providing these forecasts. First, we assess the potential of CMAQ forecasts at the single grid cell level (12 km), and show that significant variability not encountered in the field measurements occurs. This observation motivates the exploration of other data driven approaches, in particular, a neural network (NN) approach. This approach makes use of meteorology and PM2.5 observations as model predictors. We find that this approach generally results in a more accurate prediction of future pollution levels at the 12 km spatial resolution scale of CMAQ. Furthermore, we find that the NN is able to adjust to the sharp transitions encountered in pollution transported events, such as smoke plumes from forest fires, more accurately than CMAQ. Full article
(This article belongs to the Special Issue Air Quality Monitoring and Forecasting) Printed Edition available
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Open AccessArticle An Interactive Web Mapping Visualization of Urban Air Quality Monitoring Data of China
Atmosphere 2017, 8(8), 148; https://doi.org/10.3390/atmos8080148
Received: 7 July 2017 / Revised: 6 August 2017 / Accepted: 10 August 2017 / Published: 13 August 2017
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Abstract
In recent years, main cities in China have been suffering from hazy weather, which is gaining great attention among the public, government managers and researchers in different areas. Many studies have been conducted on the topic of urban air quality to reveal different
[...] Read more.
In recent years, main cities in China have been suffering from hazy weather, which is gaining great attention among the public, government managers and researchers in different areas. Many studies have been conducted on the topic of urban air quality to reveal different aspects of the air quality problem in China. This paper focuses on the visualization problem of the big air quality monitoring data of all main cities on a nationwide scale. To achieve the intuitive visualization of this data set, this study develops two novel visualization tools for multi-granularity time series visualization (timezoom.js) and a dynamic symbol declutter map mashup layer for thematic mapping (symadpative.js). With the two invented tools, we develops an interactive web map visualization application of urban air quality data of all main cities in China. This application shows us significant air pollution findings at the nationwide scale. These results give us clues for further studies on air pollutant characteristics, forecasting and control in China. As the tools are invented for general visualization purposes of geo-referenced time series data, they can be applied to other environmental monitoring data (temperature, precipitation, etc.) through some configurations. Full article
(This article belongs to the Special Issue Air Quality Monitoring and Forecasting) Printed Edition available
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Open AccessArticle Improving PM2.5 Air Quality Model Forecasts in China Using a Bias-Correction Framework
Atmosphere 2017, 8(8), 147; https://doi.org/10.3390/atmos8080147
Received: 11 July 2017 / Revised: 5 August 2017 / Accepted: 9 August 2017 / Published: 13 August 2017
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Abstract
Chinese cities are experiencing severe air pollution in particular, with extremely high PM2.5 levels observed in cold seasons. Accurate forecasting of occurrence of such air pollution events in advance can help the community to take action to abate emissions and would ultimately
[...] Read more.
Chinese cities are experiencing severe air pollution in particular, with extremely high PM2.5 levels observed in cold seasons. Accurate forecasting of occurrence of such air pollution events in advance can help the community to take action to abate emissions and would ultimately benefit the citizens. To improve the PM2.5 air quality model forecasts in China, we proposed a bias-correction framework that utilized the historic relationship between the model biases and forecasted and observational variables to post-process the current forecasts. The framework consists of four components: (1) a feature selector that chooses the variables that are informative to model forecast bias based on historic data; (2) a classifier trained to efficiently determine the forecast analogs (clusters) based on clustering analysis, such as the distance-based method and the classification tree, etc.; (3) an error estimator, such as the Kalman filter, to predict model forecast errors at monitoring sites based on forecast analogs; and (4) a spatial interpolator to estimate the bias correction over the entire modeling domain. One or more methods were tested for each step. We applied five combinations of these methods to PM2.5 forecasts in 2014–2016 over China from the operational AiMa air quality forecasting system using the Community Multiscale Air Quality (CMAQ) model. All five methods were able to improve forecast performance in terms of normalized mean error (NME) and root mean square error (RMSE), though to a relatively limited degree due to the rapid changing of emission rates in China. Among the five methods, the CART-LM-KF-AN (a Classification And Regression Trees-Linear Model-Kalman Filter-Analog combination) method appears to have the best overall performance for varied lead times. While the details of our study are specific to the forecast system, the bias-correction framework is likely applicable to the other air quality model forecast as well. Full article
(This article belongs to the Special Issue Air Quality Monitoring and Forecasting) Printed Edition available
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Open AccessArticle Air Quality and Control Measures Evaluation during the 2014 Youth Olympic Games in Nanjing and its Surrounding Cities
Atmosphere 2017, 8(6), 100; https://doi.org/10.3390/atmos8060100
Received: 28 April 2017 / Revised: 2 June 2017 / Accepted: 3 June 2017 / Published: 4 June 2017
Cited by 2 | PDF Full-text (9289 KB) | HTML Full-text | XML Full-text
Abstract
Air pollution had become a vital concern for the 2014 Youth Olympic Games in Nanjing. In order to control air pollutant emissions and ensure better air quality during the Games, the Nanjing municipal government took a series of aggressive control measures to reduce
[...] Read more.
Air pollution had become a vital concern for the 2014 Youth Olympic Games in Nanjing. In order to control air pollutant emissions and ensure better air quality during the Games, the Nanjing municipal government took a series of aggressive control measures to reduce pollutant emissions in Nanjing and its surrounding cities during the Youth Olympic Games. The Air Quality Index (AQI) is an index of air quality which is used to inform the public about levels of air pollution and associated health risks. In this study, we use the AQI and air pollutant concentrations data to evaluate the effectiveness of the implementation of control measures. The results suggest that the emission reduction measures significantly improved air quality in Nanjing. In August 2014, the mean concentrations of PM2.5, PM10, SO2, NO2, CO and O3 were 42.44 μg·m−3, 59.01 μg·m−3, 11.12 μg·m−3, 31.09 μg·m−3, 0.76 mg·m−3 and 38.39 μg·m−3, respectively, and fell by 35.92%, 36.75%, 20.40%, 15.05%, 8.54% and 47.15%, respectively, compared to the prophase mean before the emission reduction. After the emission reduction, the mean concentrations of PM2.5, PM10, SO2, NO2, and O3 increased by 20.81%, 41.84%, 22.84%, 21.16% and 60.93%, respectively, which is due to the cancellation of temporary atmospheric pollution control measures. The air pollutants diurnal variation curve during the emission reduction was lower than the other two periods, except for CO. In addition, the AQI of Nanjing and its surrounding cities showed a downward trend, compared with July 2014. The most of effective method to control air pollution is to implement the measures of regional cooperation and joint defense and control, and reduce local emissions during the polluted period, such as airborne dust, coal-burning, vehicle emissions, mobile sources and industrial production. Full article
(This article belongs to the Special Issue Air Quality Monitoring and Forecasting) Printed Edition available
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Open AccessArticle Mobile DOAS Observations of Tropospheric NO2 Using an UltraLight Trike and Flux Calculation
Atmosphere 2017, 8(4), 78; https://doi.org/10.3390/atmos8040078
Received: 24 February 2017 / Revised: 18 April 2017 / Accepted: 19 April 2017 / Published: 22 April 2017
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Abstract
In this study, we report on airborne Differential Optical Absorption Spectroscopy (DOAS) observations of tropospheric NO2 using an Ultralight Trike (ULT) and associated flux calculations. The instrument onboard the ULT was developed for measuring the tropospheric NO2 Vertical Column Density (VCD)
[...] Read more.
In this study, we report on airborne Differential Optical Absorption Spectroscopy (DOAS) observations of tropospheric NO2 using an Ultralight Trike (ULT) and associated flux calculations. The instrument onboard the ULT was developed for measuring the tropospheric NO2 Vertical Column Density (VCD) and it was operated for several days between 2011 and 2014, in the South-East of Romania. Collocated measurements were performed using a car-DOAS instrument. Most of the airborne and mobile ground-based measurements were performed close to an industrial platform located nearby Galati city (45.43° N, 28.03° E). We found a correlation of R = 0.71 between tropospheric NO2 VCDs deduced from airborne DOAS observations and mobile ground-based DOAS observations. We also present a comparison between stratospheric NO2 Slant Column Density (SCD) derived from the Dutch OMI NO2 (DOMINO) satellite data product and stratospheric SCDs obtained from ground and airborne measurements. The airborne DOAS observations performed on 13 August 2014 were used to quantify the NO2 flux originating from an industrial platform located nearby Galati city. Measurements during a flight above the industrial plume showed a maximum tropospheric NO2 VCD of (1.41 ± 0.27) × 1016 molecules/cm2 and an associated NO2 flux of (3.45 ± 0.89) × 10−3 kg/s. Full article
(This article belongs to the Special Issue Air Quality Monitoring and Forecasting) Printed Edition available
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Review

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Open AccessReview Interpreting Mobile and Handheld Air Sensor Readings in Relation to Air Quality Standards and Health Effect Reference Values: Tackling the Challenges
Atmosphere 2017, 8(10), 182; https://doi.org/10.3390/atmos8100182
Received: 17 August 2017 / Revised: 12 September 2017 / Accepted: 14 September 2017 / Published: 21 September 2017
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Abstract
The US Environmental Protection Agency (EPA) and other federal agencies face a number of challenges in interpreting and reconciling short-duration (seconds to minutes) readings from mobile and handheld air sensors with the longer duration averages (hours to days) associated with the National Ambient
[...] Read more.
The US Environmental Protection Agency (EPA) and other federal agencies face a number of challenges in interpreting and reconciling short-duration (seconds to minutes) readings from mobile and handheld air sensors with the longer duration averages (hours to days) associated with the National Ambient Air Quality Standards (NAAQS) for the criteria pollutants-particulate matter (PM), ozone, carbon monoxide, lead, nitrogen oxides, and sulfur oxides. Similar issues are equally relevant to the hazardous air pollutants (HAPs) where chemical-specific health effect reference values are the best indicators of exposure limits; values which are often based on a lifetime of continuous exposure. A multi-agency, staff-level Air Sensors Health Group (ASHG) was convened in 2013. ASHG represents a multi-institutional collaboration of Federal agencies devoted to discovery and discussion of sensor technologies, interpretation of sensor data, defining the state of sensor-related science across each institution, and provides consultation on how sensors might effectively be used to meet a wide range of research and decision support needs. ASHG focuses on several fronts: improving the understanding of what hand-held sensor technologies may be able to deliver; communicating what hand-held sensor readings can provide to a number of audiences; the challenges of how to integrate data generated by multiple entities using new and unproven technologies; and defining best practices in communicating health-related messages to various audiences. This review summarizes the challenges, successes, and promising tools of those initial ASHG efforts and Federal agency progress on crafting similar products for use with other NAAQS pollutants and the HAPs. NOTE: The opinions expressed are those of the authors and do not necessary represent the opinions of their Federal Agencies or the US Government. Mention of product names does not constitute endorsement. Full article
(This article belongs to the Special Issue Air Quality Monitoring and Forecasting) Printed Edition available
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Other

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Open AccessLetter Temporal and Spatial Patterns of China’s Main Air Pollutants: Years 2014 and 2015
Atmosphere 2017, 8(8), 137; https://doi.org/10.3390/atmos8080137
Received: 20 June 2017 / Revised: 20 July 2017 / Accepted: 25 July 2017 / Published: 27 July 2017
Cited by 7 | PDF Full-text (7148 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
China faces unprecedented air pollution today. In this study, a database (SO2, NO2, CO, O3, PM2.5 (particulate matter with aerodynamic diameter less than 2.5 μm), and PM10 (particulate matter with aerodynamic diameter less than 10
[...] Read more.
China faces unprecedented air pollution today. In this study, a database (SO2, NO2, CO, O3, PM2.5 (particulate matter with aerodynamic diameter less than 2.5 μm), and PM10 (particulate matter with aerodynamic diameter less than 10 μm) was developed from recordings in 188 cities across China in 2014 and 2015 to explore the spatial-temporal characteristics, relationships among atmospheric contaminations, and variations in these contaminants. Across China, the results indicated that the average monthly concentrations of air pollutants were higher from November to February than in other months. Further, the spatial patterns of air pollutants showed that the most polluted areas were located in Shandong, Henan, and Shanxi provinces, and the Beijing-Tianjin-Hebei region. In addition, the average daily concentrations of air pollutants were also higher in spring and winter, and significant relationships between the principal air pollutants (negative for O3 and positive for the others) were found. Finally, the results of a generalized additive model (GAM) indicated that the concentrations of PM10 and O3 fluctuate dynamically; there was a consistent increase in CO and NO2, and PM2.5 and SO2 showed a sharply decreasing trend. To minimize air pollution, open biomass burning should be prohibited, the energy efficiency of coal should be improved, and the full use of clean fuels (nuclear, wind, and solar energy) for municipal heating should be encouraged from November to February. Consequently, an optimized program of urban development should be highlighted. Full article
(This article belongs to the Special Issue Air Quality Monitoring and Forecasting) Printed Edition available
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