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 Satellite Retrieval of Air Pollution Changes in Central and Eastern China during COVID-19 Lockdown Based on a Machine Learning Model

2021 ◽  
Vol 13 (13) ◽  
pp. 2525
Author(s):  
Zigeng Song ◽  
Yan Bai ◽  
Difeng Wang ◽  
Teng Li ◽  
Xianqiang He
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
Air Pollutants ◽  
Action Plan ◽  
Eastern China ◽  
Validation Dataset ◽  
Machine Learning Model ◽  
Clean Air ◽  
Pollution Emissions ◽  
Air Pollution Emissions

With the implementation of the 2018–2020 Clean Air Action Plan (CAAP) the and impact from COVID-19 lockdowns in 2020, air pollution emissions in central and eastern China have decreased markedly. Here, by combining satellite remote sensing, re-analysis, and ground-based observational data, we established a machine learning (ML) model to analyze annual and seasonal changes in primary air pollutants in 2020 compared to 2018 and 2019 over central and eastern China. The root mean squared errors (RMSE) for the PM2.5, PM10, O3, and CO validation dataset were 9.027 μg/m3, 20.312 μg/m3, 10.436 μg/m3, and 0.097 mg/m3, respectively. The geographical random forest (RF) model demonstrated good performance for four main air pollutants. Notably, PM2.5, PM10, and CO decreased by 44.1%, 43.2%, and 35.9% in February 2020, which was likely influenced by the COVID-19 lockdown and primarily lasted until May 2020. Furthermore, PM2.5, PM10, O3, and CO decreased by 16.4%, 24.2%, 2.7%, and 19.8% in 2020 relative to the average values in 2018 and 2019. Moreover, the reduction in O3 emissions was not universal, with a significant increase (~20–40%) observed in uncontaminated areas.

The Calculation of Air Pollutants Emission from Ships in Ports

2014 ◽  
Vol 1065-1069 ◽  
pp. 3105-3109
Author(s):  
Ya Qian Zhao ◽  
Wei Wang ◽  
Xue Jun Feng
Keyword(s):  
Air Pollution ◽  
Fuel Consumption ◽  
Air Pollutants ◽  
Pollutants Emission ◽  
Pollution Emissions ◽  
Air Pollution Emissions

The air pollutants emissions from ships obtained a large proportion in the system. The research of air pollutants from ships has become a hot issue. The paper analyzes the generating mechanism and detriment of air pollution from ships, and summarizes the methods to calculate air pollution emissions in ports, clearly defined the concepts and details the formulas of the method based on fuel consumption and the method based on power, finally propose reasonable methods to calculate the ship air pollutants under different conditions, to improve the convenience and accuracy of calculation.

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 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 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 STUDY OF IMPACT OF AIR POLLUTION ON PLANTS BY ESTIMATION OF APTI OF CERTAIN TREES IN KATRAJ AREA IN PUNE CITY

2020 ◽  
pp. 39-43
Author(s):  
Erum F H Kazi ◽  
Dr. Satish Kulkarni
Keyword(s):  
Air Pollution ◽  
Ascorbic Acid ◽  
Particulate Matter ◽  
Water Content ◽  
Relative Water Content ◽  
Air Pollutants ◽  
Total Chlorophyll ◽  
Mango Tree ◽  
Relative Water ◽  
Leaf Relative Water Content

Air pollution is one of major concerns in Pune City currently. Study highlights increase in Particulate matter from Vehicular sources & Urbanization in Karaj area is having harmful impact on the trees in the area. Leaf of Plant species such as Peepal( Ficusreligiosa),, Tamarind(Tamarindusindica), Rain tree( Samaneasaman), Ashoka( Saracaasoca), Manago( Mangiferaindica), Almond( Terminaliacatappa) , Banyan tree(Ficusbenghalensis) were selected and it was found that Ashoka( Saracaasoca), Mango tree( Mangiferaindica) showed Intermediate APTI whereas Peepal, Tamarind, Rain tree, Almond, Banyan tree were found to be Sensitive to pollution. KEYWORDS: Air Pollutants, APTI of plants, Total Chlorophyll, Ascorbic acid, p H of leaf, Relative water Content ( RWC)

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