scholarly journals The MAPM (Mapping Air Pollution eMissions) method for inferring particulate matter emissions maps at city scale from in situ concentration measurements: description and demonstration of capability

2021 ◽  
Vol 21 (18) ◽  
pp. 14089-14108
Author(s):  
Brian Nathan ◽  
Stefanie Kremser ◽  
Sara Mikaloff-Fletcher ◽  
Greg Bodeker ◽  
Leroy Bird ◽  
...  
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Inverse Model ◽  
Bottom Up ◽  
Particulate Matter Emissions ◽  
Concentration Measurements ◽  
Pollution Emissions ◽  
Air Pollution Emissions ◽  
Pm2.5 Emissions

Abstract. Mapping Air Pollution eMissions (MAPM) is a 2-year project whose goal is to develop a method to infer particulate matter (PM) emissions maps from in situ PM concentration measurements. Central to the functionality of MAPM is an inverse model. The input of the inverse model includes a spatially distributed prior emissions estimate and PM measurement time series from instruments distributed across the desired domain. In this proof-of-concept study, we describe the construction of this inverse model, the mathematics underlying the retrieval of the resultant posterior PM emissions maps, the way in which uncertainties are traced through the MAPM processing chain, and plans for future developments. To demonstrate the capability of the inverse model developed for MAPM, we use the PM2.5 measurements obtained during a dedicated winter field campaign in Christchurch, New Zealand, in 2019 to infer PM2.5 emissions maps on a city scale. The results indicate a systematic overestimation in the prior emissions for Christchurch of at least 40 %–60 %, which is consistent with some of the underlying assumptions used in the composition of the bottom-up emissions map used as the prior, highlighting the uncertainties in bottom-up approaches for estimating PM2.5 emissions maps.

scholarly journals The MAPM (Mapping Air Pollution eMissions) method for inferring particulate matter emissions maps at city-scale from in situ concentration measurements: description and demonstration of capability

2021 ◽  
Author(s):  
Brian Nathan ◽  
Stefanie Kremser ◽  
Sara Mikaloff-Fletcher ◽  
Greg Bodeker ◽  
Leroy Bird ◽  
...  
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Inverse Model ◽  
Bottom Up ◽  
Processing Chain ◽  
Concentration Measurements ◽  
Pollution Emissions ◽  
Air Pollution Emissions ◽  
Pm2.5 Emissions

Abstract. MAPM (Mapping Air Pollution eMissions) is a two-year project whose goal is to develop a method to infer particulate matter (PM) emissions maps from in situ PM concentration measurements. Central to the functionality of MAPM is an inverse model. The input of the inverse model includes a spatially-distributed prior emissions estimate and PM measurement time series from instruments distributed across the desired domain. Here we describe the construction of this inverse model, the mathematics underlying the retrieval of the resultant posterior PM emissions maps, the way in which uncertainties are traced through the MAPM processing chain, and plans for future development of the processing chain. To demonstrate the capability of the inverse model developed for MAPM, we use the PM2.5 measurements obtained during a dedicated winter field campaign in Christchurch, New Zealand, in 2019 to infer PM2.5 emissions maps on city scale. The results indicate a systematic overestimation in the prior emissions for Christchurch of at least 40–60 %, which is consistent with some of the underlying assumptions used in the composition of the bottom-up emissions map used as the prior, highlighting the uncertainties in bottom-up approaches for estimating PM2.5 emissions maps. The paper also presents the results of two sets of observing system simulation experiments (OSSEs) that explore how measurement uncertainties affect the computation of the derived emissions maps, and the extent to which using emissions maps from one day as the prior for the next day improves the ability of the inversion system to characterize the emissions sources. We find in the first case that a smaller number of high-accuracy instruments performs significantly better than a higher number of low-accuracy instruments. In the second case, the results are ultimately inconclusive, showing the need for further investigations that are beyond the scope of this study.

Mapping Air Pollution eMissions (MAPM)

2020 ◽  
Author(s):  
Stefanie Kremser ◽  
Sara Mikaloff-Fletcher ◽  
Brian Nathan ◽  
Ethan Dale ◽  
Jordis Tradowsky ◽  
...  
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Meteorological Data ◽  
Inverse Model ◽  
Atmospheric Measurements ◽  
Field Campaign ◽  
Pm 2.5 ◽  
Pollution Emissions ◽  
Air Pollution Emissions

<p>The growth of megacities from global urbanization has degraded urban air quality sufficient to impede economic growth and create a public health hazard. Emissions of particulate matter, photochemically reactive gases, and long-lived greenhouse gases, contribute to the urban environmental footprint with concomitant economic and social costs. Mitigation actions rely critically on knowing where these emissions occur. In response to this challenge, our team has developed a new method, MAPM (Mapping Air Pollution eMissions), to generate near real-time surface emissions maps of particulate matter pollution. Surface particulate matter (PM 2.5) emission maps will be derived from atmospheric measurements of particulate matter using an inverse model in conjunction with a state-of-the-art mesoscale atmospheric model.</p><p>The MAPM methodology is validated and refined using particulate matter measurements made during a field campaign that took place in Christchurch, New Zealand from June to September 2019. Key questions that MAPM aims to answer include:</p><ul><li>How do uncertainties on the PM 2.5 measurements affect the quality of the emissions maps we extract from our inverse model.</li> <li>How do uncertainties in the meteorological data affect the quality of the emissions maps we extract from our inverse model.</li> <li>How does the spatial and temporal resolution of the air pollution concentration measurements affect the uncertainties in the retrieved pollution emissions maps?</li> </ul><p>Here we will not only present the measurements made during the winter field campaign but also present the first derived PM 2.5 emissions maps for the city of Christchurch.</p>

scholarly journals Decomposition of air pollution emissions from Swedish manufacturing

2021 ◽  
Author(s):  
Polina Ustyuzhanina
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Volatile Compounds ◽  
Air Pollutants ◽  
Technique Effect ◽  
Main Driver ◽  
Pollution Emissions ◽  
Air Pollution Emissions

AbstractStarting from the ’90s, Swedish manufacturing output has been constantly growing, while emissions of some major air pollutants have been declining. This paper decomposes manufacturing pollution emissions to identify the forces associated with the abatement. It uses a newly available dataset on actual annual emissions from Swedish manufacturing and creates an index of emission intensities for the major local air pollutants to directly estimate the technique effect for the period 2007–2017. The results suggest that the main driver of the clean-up was improvements in emission intensities, while the composition of output actually moved towards more pollution-intensive goods. In the absence of changes in scale and technique, manufacturing pollution emissions would have increased in a range between 3 (particulate matter) and 20% (non-methane volatile compounds) between 2007 and 2017.

scholarly journals The winter 2019 air pollution (PM<sub>2.5</sub>) measurement campaign in Christchurch, New Zealand

2020 ◽  
Author(s):  
Ethan R. Dale ◽  
Stefanie Kremser ◽  
Jordis S. Tradowsky ◽  
Greg E. Bodeker ◽  
Leroy J. Bird ◽  
...  
Keyword(s):  
Air Pollution ◽  
New Zealand ◽  
Anthropogenic Sources ◽  
Measurement Campaign ◽  
Different Types ◽  
Pm2.5 And Pm10 ◽  
Pollution Emissions ◽  
Air Pollution Emissions ◽  
Made In

Abstract. MAPM (Mapping Air Pollution eMissions) is a project whose goal is to develop a method to infer particulate matter (PM) emissions maps from in situ PM concentration measurements. In support of MAPM, a winter field campaign was conducted in New Zealand in 2019 (June to September) to obtain the measurements required to test and validate the MAPM methodology. Two different types of instruments measuring PM were deployed: ES-642 remote dust monitors (17 instruments) and Outdoor Dust Information Nodes (ODINs; 50 instruments). The measurement campaign was bracketed by two intercomparisons where all instruments were co-located, with a permanently installed Tapered Element Oscillating Membrane (TEOM) instrument, to determine any instrument biases. Changes in biases between the pre- and post-campaign intercomparisons were used to determine instrument drift over the campaign period. Once deployed, each ES-642 was co-located with an ODIN. In addition to the PM measurements, meteorological variables (temperature, pressure, wind speed and wind direction) were measured at three automatic weather station (AWS) sites established as part of the campaign, with additional data being sourced from 27 further AWSs operated by other agencies. Vertical profile measurements were made in two intensive radiosonde sub-campaigns and were supplemented with measurements made with a mini micropulse lidar and ceilometer. Here we present the data collected during the campaign and discuss the correction of the measurements made by various PM instruments. We find that for while for the ODINs a correction based on environmental conditions is beneficial, this results in over-fitting and increased uncertainties when applied to the measurements obtained using the more sophisticated ES-642s. We also compare PM2.5 and PM10 measured by ODINs which, in some cases, allows us to identify PM from natural and anthropogenic sources. The PM data collected during the campaign are publicly available from https://doi.org/10.5281/zenodo.4023402 (Dale et. al., 2020b), the data from other instruments are available from https://doi.org/10.5281/zenodo.4021685 (Dale et. al., 2020a).

scholarly journals COVID-19 and Air Pollution: Measuring Pandemic Impact to Air Quality in Five European Countries

Atmosphere ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 290
Author(s):  
Akvilė Feiferytė Skirienė ◽  
Žaneta Stasiškienė
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Social Services ◽  
Pollution Monitoring ◽  
Air Pollution Monitoring ◽  
Rapid Spread ◽  
Air Quality Improvement ◽  
Environment Agency ◽  
Pollution Emissions ◽  
Air Pollution Emissions

The rapid spread of the coronavirus (COVID-19) pandemic affected the economy, trade, transport, health care, social services, and other sectors. To control the rapid dispersion of the virus, most countries imposed national lockdowns and social distancing policies. This led to reduced industrial, commercial, and human activities, followed by lower air pollution emissions, which caused air quality improvement. Air pollution monitoring data from the European Environment Agency (EEA) datasets were used to investigate how lockdown policies affected air quality changes in the period before and during the COVID-19 lockdown, comparing to the same periods in 2018 and 2019, along with an assessment of the Index of Production variation impact to air pollution changes during the pandemic in 2020. Analysis results show that industrial and mobility activities were lower in the period of the lockdown along with the reduced selected pollutant NO2, PM2.5, PM10 emissions by approximately 20–40% in 2020.

scholarly journals Status of Air Pollution Regulations Affecting Gas Turbines in 80 Nations

1974 ◽  
Author(s):  
R. J. Ketterer ◽  
N. R. Dibelius
Keyword(s):  
Air Pollution ◽  
Carbon Monoxide ◽  
Sulfur Dioxide ◽  
Gas Turbines ◽  
Ground Level ◽  
Oxides Of Nitrogen ◽  
Ground Level Concentration ◽  
Pollution Emissions ◽  
Air Pollution Emissions

This paper summarizes regulations from 80 countries covering air pollution emissions from gas turbines. The paper includes emission and ground level concentration standards for particulates, sulfur dioxide, oxides of nitrogen, visible emissions, and carbon monoxide.

scholarly journals Air Pollution and Human Sperm Sex Ratio

2018 ◽  
Vol 12 (4) ◽  
pp. 907-912 ◽  
Author(s):  
Michał Radwan ◽  
Emila Dziewirska ◽  
Paweł Radwan ◽  
Lucjusz Jakubowski ◽  
Wojciech Hanke ◽  
...  
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Sex Ratio ◽  
Human Sperm ◽  
Quality Data ◽  
Aerodynamic Diameter ◽  
Limited Data ◽  
Study Population ◽  
Air Quality Data

The present study was designed to address the hypothesis that exposure to specific air pollutants may impact human sperm Y:X chromosome ratio. The study population consisted of 195 men who were attending an infertility clinic for diagnostic purposes and who had normal semen concentration of 15–300 mln/ml (WHO, 2010). Participants represented a subset of men in a multicenter parent study conducted in Poland to evaluate environmental factors and male fertility. Participants were interviewed and provided a semen sample. The Y:X ratio was assessed by fluorescent in situ hybridization (FISH). Air quality data were obtained from the AirBase database. In multivariate analysis the significant reduction was observed in the proportion of Y/X chromosome bearing sperm and exposure to particulate matter >10 μm in aerodynamic diameter PM10 ( p = .009) and particulate matter <10 μm in aerodynamic diameter PM2.5 ( p = .023). The observed effects of a lower Y:X sperm chromosome ratio among men exposed to air pollution support the evidence that the trend of declining sex ratio in several societies over past decades has been due to exposure to air pollution; however due to limited data on this issue, the obtained results should be confirmed in longitudinal studies.

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