Meteorological Normalisation Using Boosted Regression Trees to Estimate the Impact of COVID-19 Restrictions on Air Quality Levels

The global COVID-19 pandemic that began in late December 2019 led to unprecedented lockdowns worldwide, providing a unique opportunity to investigate in detail the impacts of restricted anthropogenic emissions on air quality. A wide range of strategies and approaches exist to achieve this. In this paper, we use the “deweather” R package, based on Boosted Regression Tree (BRT) models, first to remove the influences of meteorology and emission trend patterns from NO, NO2, PM10 and O3 data series, and then to calculate the relative changes in air pollutant levels in 2020 with respect to the previous seven years (2013–2019). Data from a northern Spanish region, Cantabria, with all types of monitoring stations (traffic, urban background, industrial and rural) were used, dividing the calendar year into eight periods according to the intensity of government restrictions. The results showed mean reductions in the lockdown period above −50% for NOx, around −10% for PM10 and below −5% for O3. Small differences were found between the relative changes obtained from normalised data with respect to those from observations. These results highlight the importance of developing an integrated policy to reduce anthropogenic emissions and the need to move towards sustainable mobility to ensure safer air quality levels, as pre-existing concentrations in some cases exceed the safe threshold.

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Evaluation of Boosted Regression Tree for the Prediction of the Maximum 24-Hour Concentration of Particulate Matter

International Journal of Environmental Science and Development ◽

10.18178/ijesd.2021.12.4.1329 ◽

2021 ◽

Vol 12 (4) ◽

pp. 126-130

Author(s):

Wan Nur Shaziayani ◽

◽

Ahmad Zia Ul-Saufie ◽

Syarifah Adilah Mohamed Yusoff ◽

Hasfazilah Ahmat ◽

...

Keyword(s):

Air Quality ◽

Regression Tree ◽

Pm10 Concentration ◽

Boosted Regression Trees ◽

Concentration Data ◽

Boosted Regression Tree ◽

Independent Test ◽

Multiple Regression Models ◽

Daily Pm10 ◽

Fold Cross Validation

Air pollution is a considerable health danger to the environment. The objective of this study was to assess the characteristics of air quality and predict PM10 concentrations using boosted regression trees (BRTs). The maximum daily PM10 concentration data from 2002 to 2016 were obtained from the air quality monitoring station in Kuching, Sarawak. Eighty percent of the monitoring records were used for the training and twenty percent for the validation of the models. The best iteration of the BRT model was performed by optimizing the prediction performance, while the BRT algorithm model was constructed from multiple regression models. The two main parameters that were used were the learning rate (lr) and tree complexity (tc), which were fixed at 0.01 and 5, respectively. Meanwhile, the number of trees (nt) was determined by using an independent test set (test), a 5-fold cross validation (CV) and out-of-bag (OOB) estimation. The algorithm model for the BRT produced by using the CV was the best guide to be used compared with the OOB to test the predicted PM10 concentration. The performance indicators showed that the model was adequate for the next day’s prediction (PA=0.638, R2=0.427, IA=0.749, NAE=0.267, and RMSE=28.455).

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Developing Relative Humidity and Temperature Corrections for Low-Cost Sensors Using Machine Learning

Sensors ◽

10.3390/s21103338 ◽

2021 ◽

Vol 21 (10) ◽

pp. 3338

Author(s):

Ivan Vajs ◽

Dejan Drajic ◽

Nenad Gligoric ◽

Ilija Radovanovic ◽

Ivan Popovic

Keyword(s):

Machine Learning ◽

Air Quality ◽

Relative Humidity ◽

Low Cost ◽

Quality Monitoring ◽

Air Quality Monitoring ◽

Lower Accuracy ◽

Wide Range ◽

The Impact ◽

Monitoring Stations

Existing government air quality monitoring networks consist of static measurement stations, which are highly reliable and accurately measure a wide range of air pollutants, but they are very large, expensive and require significant amounts of maintenance. As a promising solution, low-cost sensors are being introduced as complementary, air quality monitoring stations. These sensors are, however, not reliable due to the lower accuracy, short life cycle and corresponding calibration issues. Recent studies have shown that low-cost sensors are affected by relative humidity and temperature. In this paper, we explore methods to additionally improve the calibration algorithms with the aim to increase the measurement accuracy considering the impact of temperature and humidity on the readings, by using machine learning. A detailed comparative analysis of linear regression, artificial neural network and random forest algorithms are presented, analyzing their performance on the measurements of CO, NO2 and PM10 particles, with promising results and an achieved R2 of 0.93–0.97, 0.82–0.94 and 0.73–0.89 dependent on the observed period of the year, respectively, for each pollutant. A comprehensive analysis and recommendations on how low-cost sensors could be used as complementary monitoring stations to the reference ones, to increase spatial and temporal measurement resolution, is provided.

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Gender Differences in Association between Air Pollution and Daily Mortality in the Capital of the Green Lungs of Poland–Population-Based Study with 2,953,000 Person-Years of Follow-Up

Journal of Clinical Medicine ◽

10.3390/jcm9082351 ◽

2020 ◽

Vol 9 (8) ◽

pp. 2351

Author(s):

Łukasz Kuźma ◽

Krzysztof Struniawski ◽

Szymon Pogorzelski ◽

Hanna Bachórzewska-Gajewska ◽

Sławomir Dobrzycki

Keyword(s):

Public Health ◽

Gender Differences ◽

Air Pollution ◽

Air Quality ◽

Air Pollutant ◽

World Health ◽

The European Union ◽

Daily Mortality ◽

Male Population ◽

The Impact

(1) Introduction: air pollution is considered to be one of the main risk factors for public health. According to the European Environment Agency (EEA), air pollution contributes to the premature deaths of approximately 500,000 citizens of the European Union (EU), including almost 5000 inhabitants of Poland every year. (2) Purpose: to assess the gender differences in the impact of air pollution on the mortality in the population of the city of Bialystok—the capital of the Green Lungs of Poland. (3) Materials and Methods: based on the data from the Central Statistical Office, the number—and causes of death—of Białystok residents in the period 2008–2017 were analyzed. The study utilized the data recorded by the Provincial Inspectorate for Environmental Protection station and the Institute of Meteorology and Water Management during the analysis period. Time series regression with Poisson distribution was used in statistical analysis. (4) Results: A total of 34,005 deaths had been recorded, in which women accounted for 47.5%. The proportion of cardiovascular-related deaths was 48% (n = 16,370). An increase of SO2 concentration by 1-µg/m3 (relative risk (RR) 1.07, 95% confidence interval (CI) 1.02–1.12; p = 0.005) and a 10 °C decrease of temperature (RR 1.03, 95% CI 1.01–1.05; p = 0.005) were related to an increase in the number of daily deaths. No gender differences in the impact of air pollution on mortality were observed. In the analysis of the subgroup of cardiovascular deaths, the main pollutant that was found to have an effect on daily mortality was particulate matter with a diameter of 2.5 μm or less (PM2.5); the RR for 10-µg/m3 increase of PM2.5 was 1.07 (95% CI 1.02–1.12; p = 0.01), and this effect was noted only in the male population. (5) Conclusions: air quality and atmospheric conditions had an impact on the mortality of Bialystok residents. The main air pollutant that influenced the mortality rate was SO2, and there were no gender differences in the impact of this pollutant. In the male population, an increased exposure to PM2.5 concentration was associated with significantly higher cardiovascular mortality. These findings suggest that improving air quality, in particular, even with lower SO2 levels than currently allowed by the World Health Organization (WHO) guidelines, may benefit public health. Further studies on this topic are needed, but our results bring questions whether the recommendations concerning acceptable concentrations of air pollutants should be stricter, or is there a safe concentration of SO2 in the air at all.

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To what extent does the environmental magnetism technique acts as a way of assessing the uptake of atmospheric particles by plants?

10.5194/egusphere-egu21-8263 ◽

2021 ◽

Author(s):

Sarah Letaïef ◽

Pierre Camps ◽

Thierry Poidras ◽

Patrick Nicol ◽

Delphine Bosch ◽

...

Keyword(s):

Air Quality ◽

Endemic Species ◽

Fine Particles ◽

Magnetic Measurements ◽

Deposition Velocity ◽

Environmental Magnetism ◽

Air Pollutant ◽

Atmospheric Pollutants ◽

Area Index ◽

The Impact

Numerous studies have already shown the possibility of tracing the sources, the compositions, and the concentration of atmospheric pollutants deposited on plant leaves. In environmental geochemistry, inter-element and isotope ratios from chemical element assays have been used for these purposes. Alternatively, environmental magnetism represents a quick and inexpensive asset that is increasingly used as a relative indicator for concentrations of air pollutant on bio accumulator surfaces such as plants. However, a fundamental issue is still pending: Do plants in urban areas represent a sink for fine particles that is sufficiently effective to improve air quality? This is a very topical issue because some studies have shown that the foliage can trap fine particles by different dry deposition processes, while other studies based on CFD models indicate that plant hedges in cities can hinder the atmospheric dispersion of pollutants and therefore increase pollution at the level of emission sources such as traffic. To date, no consensus was made because several factors not necessary well known must be taken into account, such as, PM concentration and size, prevailing wind, surface structures, epicuticular wax, to mention just a few examples. A first step toward the understanding of the impact of urban greens on air quality is the precise determination of the deposition velocity (Vd) parameter. This latter is specific for each species and it is most of the time underestimated in modeling-based studies by taking standard values. In that perspective, we built a wind tunnel (6 m long, 86 cm wide and 86 cm high) to perform analogical experiments on different endemic species. All parameters are controlled, i.e, the wind speed, the nature and the injection time of pollutants (Gasoline or Diesel exhausts, brakes or tires dust, etc&#8230;). We can provide the PM concentrations upwind and downwind of natural reconstituted hedges by two dustmeters (LOACs - M&#233;t&#233;oModem). Beforehand, parameters such as the hedge resistance (%) or the leaf area index (LAI) have been estimated for each studied specie to allow comparability between plants removal potential. The interest would ultimately combine PM concentration measured by size bins from the LOACs with magnetic measurements (ARM, IRM100mT, IRM300mT and SIRM) of plant leaves. The idea is to check whether it would be possible to precisely determine in situ the dust removal rate by urban greens with environmental magnetism measurements. Up to now, we have carried out on different endemic species such as Elaeagnus x ebbingei leaves and Mediterranean pine needles, the results of which will be presented.

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Avoided Mortality Associated with Improved Air Quality from an Increase in Renewable Energy in the Spanish Transport Sector: Use of Biofuels and the Adoption of the Electric Car

Atmosphere ◽

10.3390/atmos12121603 ◽

2021 ◽

Vol 12 (12) ◽

pp. 1603

Author(s):

Ana R. Gamarra ◽

Yolanda Lechón ◽

Marta G. Vivanco ◽

Mark Richard Theobald ◽

Carmen Lago ◽

...

Keyword(s):

Air Quality ◽

Health Impact ◽

Air Pollutant ◽

Transport Sector ◽

Population Exposure ◽

Baseline Scenario ◽

Premature Deaths ◽

Electric Car ◽

The Impact

This paper assesses the health impact, in terms of the reduction of premature deaths associated with changes in air pollutant exposure, resulting from double-aim strategies for reducing emissions of greenhouse gases and air pollutants from the transport sector for the year 2030 in Spain. The impact on air quality of selected measures for reducing emissions from the transport sector (increased penetration of biofuel and electric car use) was assessed by air quality modeling. The estimation of population exposure to NO2, particulate matter (PM) and O3 allows for estimation of associated mortality and external costs in comparison with the baseline scenario with no measures. The results show that the penetration of the electric vehicle provided the largest benefits, even when the emissions due to the additional electricity demand were considered.

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Air quality impacts of COVID-19 lockdown measures detected from space using high spatial resolution observations of multiple trace gases from Sentinel-5P/TROPOMI

10.5194/acp-2021-534 ◽

2021 ◽

Author(s):

Pieternel F. Levelt ◽

Deborah C. Stein Zweers ◽

Ilse Aben ◽

Maite Bauwens ◽

Tobias Borsdorff ◽

...

Keyword(s):

Air Quality ◽

Spatial Resolution ◽

High Spatial Resolution ◽

Trace Gases ◽

Trace Gas ◽

Joint Analysis ◽

Anthropogenic Emissions ◽

China And India ◽

Societal Relevance ◽

The Impact

Abstract. The aim of this paper is two-fold: to provide guidance on how to best interpret TROPOMI trace gas retrievals and to highlight how TROPOMI trace gas data can be used to understand event-based impacts on air quality from regional to city-scales around the globe. For this study, we present the observed changes in the atmospheric column amounts of five trace gases (NO2, SO2, CO, HCHO and CHOCHO) detected by the Sentinel-5P TROPOMI instrument, driven by reductions of anthropogenic emissions due to COVID-19 lockdown measures in 2020. We report clear COVID-19-related decreases in NO2 concentrations on all continents. For megacities, reductions in column amounts of tropospheric NO2 range between 14 % and 63 %. For China and India supported by NO2 observations, where the primary source of anthropogenic SO2 is coal-fired power generation, we were able to detect sector-specific emission changes using the SO2 data. For HCHO and CHOCHO, we consistently observe anthropogenic changes in two-week averaged column amounts over China and India during the early phases of the lockdown periods. That these variations over such a short time scale are detectable from space, is due to the high resolution and improved sensitivity of the TROPOMI instrument. For CO, we observe a small reduction over China which is in concert with the other trace gas reductions observed during lockdown, however large, interannual differences prevent firm conclusions from being drawn. The joint analysis of COVID-19 lockdown-driven reductions in satellite observed trace gas column amounts, using the latest operational and scientific retrieval techniques for five species concomitantly is unprecedented. However, the meteorologically and seasonally driven variability of the five trace gases does not allow for drawing fully quantitative conclusions on the reduction of anthropogenic emissions based on TROPOMI observations alone. We anticipate that in future, the combined use of inverse modelling techniques with the high spatial resolution data from S5P/TROPOMI for all observed trace gases presented here, will yield a significantly improved sector-specific, space-based analysis of the impact of COVID-19 lockdown measures as compared to other existing satellite observations. Such analyses will further enhance the scientific impact and societal relevance of the TROPOMI mission.

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NESTED HIGH-RESOLUTION NOx AND PM SIMULATIONS OVER ZÜRICH

10.5194/egusphere-egu21-6202 ◽

2021 ◽

Author(s):

Ivo Suter ◽

Lukas Emmenegger ◽

Dominik Brunner

Keyword(s):

Air Pollution ◽

Air Quality ◽

Boundary Conditions ◽

Meteorological Conditions ◽

Model Systems ◽

Multi Scale ◽

Wide Range ◽

The Impact ◽

The City ◽

Concentration Levels

Reducing air pollution, which is the world's largest single environmental health risk, demands better-informed air quality policies. Consequently, multi-scale air quality models are being developed with the goal to resolve cities. One of the major challenges in such model systems is to accurately represent all large- and regional-scale processes that may critically determine the background concentration levels over a given city. This is particularly true for longer-lived species such as aerosols, for which background levels often dominate the concentration levels, even within the city. Furthermore, the heterogeneous local emissions, and complex dispersion in the city have to be considered carefully.In this study, the impact of processes across a wide range of scales on background concentrations over Switzerland and the city of Zurich was modelled by performing one year of nested European and Swiss national COSMO-ART simulations to obtain adequate boundary conditions for gas-phase chemical, aerosol and meteorological conditions for city-resolving simulations. The regional climate chemistry model COSMO-ART (Vogel et al. 2009) was used in a 1-way coupled mode. The outer, European, domain, which was driven by chemical boundary conditions from the global MOZART model, had a 6.6 km horizontal resolution and the inner, Swiss, domain one of 2.2 km. For the city scale, a catalogue of more than 1000 mesoscale flow patterns with 100 m resolution was created with the model GRAMM, based on a discrete set of atmospheric stabilities, wind speeds and directions, accounting for the influence of land-use and topography. Finally, the flow around buildings was solved with the CFD model GRAL forced at the boundaries by GRAMM. Subsequently, Lagrangian dispersion simulations for a set of air pollutants and emission sectors (traffic, industry, ...) based on extremely detailed building and emission data was performed in GRAL. The result of this nested procedure is a library of 3-dimensional air pollution maps representative of hourly situations in Zurich (Berchet et al. 2017). From these pre-computed situations, time-series and concentration maps can be obtained by selecting situations according to observed or modelled meteorological conditions.The results were compared to measurements from air quality monitoring network stations. Modelled concentrations of NOx and PM compared well to measurements across multiple locations, provided background conditions were considered carefully. The nested multi-scale modelling system COSMO-ART/GRAMM/GRAL can adequately reproduce local air quality and help understanding the relative contributions of local versus distant emissions, as well as fill the space between precise point measurements from monitoring sites. This information is useful for research, policy-making, and epidemiological studies particularly under the assumption that exceedingly high concentrations become more and more localised phenomenon in the future.

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FRI0531 AIR POLLUTANTS AND DEVELOPMENT OF INTERSTITIAL LUNG DISEASE IN PATIENTS WITH AUTOIMMUNE DISEASES

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2020-eular.204 ◽

2020 ◽

Vol 79 (Suppl 1) ◽

pp. 865.1-865

Author(s):

H. H. Chen ◽

W. C. Chao ◽

Y. H. Chen ◽

D. Y. Chen ◽

C. H. Lin

Keyword(s):

Air Quality ◽

Autoimmune Diseases ◽

Air Pollutants ◽

Air Pollutant ◽

Air Quality Monitoring ◽

Newly Diagnosed ◽

Science Technology ◽

Air Quality Monitoring Stations ◽

The Impact ◽

Modest Level

Background:Interstitial lung disease (ILD) is characterized by progressive inflammation and fibrosis, and accumulating evidence have shown that exposure to air pollutants was associated with the development of ILD. Autoimmune diseases are highly correlated with ILD, including connective tissue disease-associated ILD (CTD-ILD) as well as interstitial pneumonia with autoimmune features (IPAF), and the development of ILD is a crucial cause of morbidity and mortality in patients with autoimmune diseases. One recent Taiwanese study reported that exposure to air pollutants was associated with incident systemic lupus erythematosus (SLE). However, the impact of air pollutants on the development of ILD among patients with autoimmune diseases remains unknown.Objectives:The study aimed to address the impact of accumulating exposure to air pollutant above moderate level, defined by Air Quality Index (AQI) value higher than 50, on the development of ILD in patients with autoimmune diseases including SLE, rheumatoid arthritis (RA) and primary Sjögren’s syndrome (SS).Methods:We used a National Health Insurance Research Database in Taiwan to enroll patients with SLE (International Classification of Diseases (ICD)-9 code 710.0, n=13,211), RA (ICD-9 code 714.0 and 714.30–714.33, n=32,373), and primary SS (ICD-9 code, 710.0, n=15,246) between 2001 and 2013. We identified newly diagnosed ILD cases (ICD-code 515) between 2012 and 2013 and selected age, sex, disease duration and index-year matched (1:4) patients as non-ILD controls. The hourly levels of air pollutants one year prior to the index-date were obtained from 60 air quality monitoring stations across Taiwan, and the air pollutants in the present study consisted of particulate matter <2.5 μm in size (PM2.5), particulate matter <10 μm in size (PM10), nitrogen dioxide (NO2), carbon monoxide (CO), sulfur dioxide (SO2) and ozone (O3). We used a spatio-temporal model built by a deep-learning mechanism to estimate levels of air pollutants at 374 residential locations based on data of 3 air quality monitoring stations near the location (8). Notably, we used cumulative exposed hours to air pollutants higher than modest level, defined by AQI criteria, given that daily mean level of air pollutants might possibly underestimate the triggered inflammatory effect by a temporary exposure of high-level air pollutant. A conditional logistic regression was used to determine the association between exposure to air pollutant and the development of ILD, adjusting age, gender, Charlson Comorbidity Index (CCI), urbanization, family income, and medications for autoimmune diseases.Results:A total of 272 patients with newly diagnosed ILD were identified among patients with autoimmune diseases, including 39 with SLE, 135 with RA, and 98 with primary SS. We found that the duration of exposure to PM 2.5 higher than modest level was associated with the risk of ILD development in patients with SS (adjOR 1.07, 95% CI 1.01–1.13), and similar trends were also found in patients with SLE (adjOR 1.03, 95% CI 0.95–1.12) and RA (adjOR 1.03, 95% CI 0.99–1.07). Intriguingly, we observed an inverse correlation between the duration of exposure to O3 and the development of ILD in patients with SS (adjOR 0.83, 95% CI 0.70–0.99); however, the finding was not found in patients with SLE (adjOR 1.13, 95% CI 0.92–1.37) and RA (adjOR 0.98, 95% CI 0.87–1.11).Conclusion:In conclusion, we identified that longer exposure to PM2.5 higher than modest level tended to be associated with the development of ILD in patients with autoimmune diseases, mainly SS.References:[1] Araki T, Putman RK, Hatabu H, Gao W, Dupuis J, Latourelle JC, et al. Development and Progression of Interstitial Lung Abnormalities in the Framingham Heart Study. Am J Respir Crit Care Med 2016;194:1514-1522.[2] Tang KT, Tsuang BJ, Ku KC, Chen YH, Lin CH, Chen DY. Relationship between exposure to air pollutants and development of systemic autoimmune rheumatic diseases: a nationwide population-based case-control study. Ann Rheum Dis 2019;78:1288-1291.Disclosure of Interests:Hsin-Hua Chen: None declared, Wen-Cheng Chao: None declared, Yi-Hsing Chen Grant/research support from: Taiwan Ministry of Science and Technology, Taiwan Department of Health, Taichung Veterans General Hospital, National Yang-Ming University, GSK, Pfizer, BMS., Consultant of: Pfizer, Novartis, Abbvie, Johnson & Johnson, BMS, Roche, Lilly, GSK, Astra& Zeneca, Sanofi, MSD, Guigai, Astellas, Inova Diagnostics, UCB, Agnitio Science Technology, United Biopharma, Thermo Fisher, Gilead., Paid instructor for: Pfizer, Novartis, Johnson & Johnson, Roche, Lilly, Astra& Zeneca, Sanofi, Astellas, Agnitio Science Technology, United Biopharma., Speakers bureau: Pfizer, Novartis, Abbvie, Johnson & Johnson, BMS, Roche, Lilly, GSK, Astra& Zeneca, Sanofi, MSD, Guigai, Astellas, Inova Diagnostics, UCB, Agnitio Science Technology, United Biopharma, Thermo Fisher, Gilead., Der-Yuan Chen: None declared, Ching-Heng Lin: None declared

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Investigation of the Impact of Land-Use Distribution on PM2.5 in Weifang: Seasonal Variations

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17145135 ◽

2020 ◽

Vol 17 (14) ◽

pp. 5135

Author(s):

Chengming Li ◽

Kuo Zhang ◽

Zhaoxin Dai ◽

Zhaoting Ma ◽

Xiaoli Liu

Keyword(s):

Land Use ◽

Air Pollution ◽

Air Quality ◽

Nonlinear Effects ◽

Policy Implications ◽

Effective Control ◽

Boosted Regression Tree ◽

Construction Land ◽

The Impact ◽

Effects Of Land Use

As air pollution becomes highly focused in China, the accurate identification of its influencing factors is critical for achieving effective control and targeted environmental governance. Land-use distribution is one of the key factors affecting air quality, and research on the impact of land-use distribution on air pollution has drawn wide attention. However, considerable studies have mostly used linear regression models, which fail to capture the nonlinear effects of land-use distribution on PM2.5 (fine particulate matter with a diameter less than or equal to 2.5 microns) and to show how impacts on PM2.5 vary with land-use magnitudes. In addition, related studies have generally focused on annual analyses, ignoring the seasonal variability of the impact of land-use distribution on PM2.5, thus leading to possible estimation biases for PM2.5. This study was designed to address these issues and assess the impacts of land-use distribution on PM2.5 in Weifang, China. A machine learning statistical model, the boosted regression tree (BRT), was applied to measure nonlinear effects of land-use distribution on PM2.5, capture how land-use magnitude impacts PM2.5 across different seasons, and explore the policy implications for urban planning. The main conclusions are that the air quality will significantly improve with an increase in grassland and forest area, especially below 8% and 20%, respectively. When the distribution of construction land is greater than around 10%, the PM2.5 pollution can be seriously substantially increased with the increment of their areas. The impact of gardens and farmland presents seasonal characteristics. It is noted that as the weather becomes colder, the inhibitory effect of vegetation distribution on the PM2.5 concentration gradually decreases, while the positive impacts of artificial surface distributions, such as construction land and roads, are aggravated because leaves drop off in autumn (September–November) and winter (December–February). According to the findings of this study, it is recommended that Weifang should strengthen pollution control in winter, for instance, expand the coverage areas of evergreen vegetation like Pinus bungeana Zucc. and Euonymus japonicus Thunb, and increase the width and numbers of branches connecting different main roads. The findings also provide quantitative and optimal land-use planning and strategies to minimize PM2.5 pollution, referring to the status of regional urbanization and greening construction.

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Formaldehyde Formation in Large Bore Engines Part 2: Factors Affecting Measured CH2O

Journal of Engineering for Gas Turbines and Power ◽

10.1115/1.1290586 ◽

1999 ◽

Vol 122 (4) ◽

pp. 611-616 ◽

Cited By ~ 6

Author(s):

Daniel B. Olsen ◽

Charles E. Mitchell

Keyword(s):

Natural Gas ◽

Air Pollutant ◽

Operating Conditions ◽

Factors Affecting ◽

Natural Gas Engines ◽

Spark Timing ◽

Wide Range ◽

Hazardous Air Pollutant ◽

Relationship Of ◽

The Impact

Current research shows that the only hazardous air pollutant of significance emitted from large bore natural gas engines is formaldehyde CH2O. A literature review on formaldehyde formation is presented focusing on the interpretation of published test data and its applicability to large bore natural gas engines. The relationship of formaldehyde emissions to that of other pollutants is described. Formaldehyde is seen to have a strong correlation to total hydrocarbon (THC) level in the exhaust. It is observed that the ratio of formaldehyde to THC concentration is roughly 1.0–2.5 percent for a very wide range of large bore engines and operating conditions. The impact of engine operating parameters, load, rpm, spark timing, and equivalence ratio, on formaldehyde emissions is also evaluated. [S0742-4795(00)01004-8]

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