Canadian Air Quality Forecasting and Information Systems

Canadian Air Quality Forecasting and Information SystemsEnvironment and Climate Change Canada (ECCC) has been in charge of the national air quality program for more than 20 years. As of today, air pollution remains one of the most important environmental risk factors to health, in addition to hazardous effects on climate change, ecosystems, properties, and food and water chain.Currently, Canadian air quality forecasting and information systems with observational and modeling components are a key element for policy and mitigation measures, which are used to reduce the negative impacts of air pollution. The operational ECCC&#8217;s air quality program provides immediate adaptive measures based on early warning services. In addition to this operational service, the air quality scenario and policy modelling is essential for implementing cost-effective emission reduction strategies and local planning to ensure compliance with air quality standards.Canadian air quality forecasting and information systems also enable access to air quality data at different temporal and spatial scales. This is done through coordination of national activities to facilitate seamless provision of atmospheric composition information at various scales. This work will present Canadian air quality forecasting and information systems, components, collaboration, application and data streaming, as an example that can be helpful in building the WMO GAFIS initiative.

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Understanding Public Response to Air Quality Using Tweet Analysis

Social Media + Society ◽

10.1177/2056305119867656 ◽

2019 ◽

Vol 5 (3) ◽

pp. 205630511986765

Author(s):

Supraja Gurajala ◽

Suresh Dhaniyala ◽

Jeanna N. Matthews

Keyword(s):

Air Pollution ◽

Air Quality ◽

Machine Learning Algorithms ◽

Mitigation Measures ◽

Quality Data ◽

Public Health Issue ◽

New Delhi ◽

Public Response ◽

Pollution Levels ◽

Adverse Health Effects

Poor air quality is recognized as a major risk factor for human health globally. Critical to addressing this important public-health issue is the effective dissemination of air quality data, information about adverse health effects, and the necessary mitigation measures. However, recent studies have shown that even when public get data on air quality and understand its importance, people do not necessarily take actions to protect their health or exhibit pro-environmental behaviors to address the problem. Most existing studies on public attitude and response to air quality are based on offline studies, with a limited number of survey participants and over a limited number of geographical locations. For a larger survey size and a wider set of locations, we collected Twitter data for a period of nearly 2 years and analyzed these data for three major cities: Paris, London, and New Delhi. We identify the three hashtags in each city that best correlate the frequency of tweets with local air quality. Using tweets with these hashtags, we determined that people’s response to air quality across all three cities was nearly identical when considering relative changes in air pollution. Using machine-learning algorithms, we determined that health concerns dominated public response when air quality degraded, with the strongest increase in concern being in New Delhi, where pollution levels are the highest among the three cities studied. The public call for political solutions when air quality worsens is consistent with similar findings with offline surveys in other cities. We also conducted an unsupervised learning analysis to extract topics from tweets in Delhi and studied their evolution over time and with changing air quality. Our analysis helped extract relevant words or features associated with different air quality–related topics such as air pollution policy and health. Also, the topic modeling analysis revealed niche topics associated with sporadic air quality events, such as fireworks during festivals and the air quality impact on an outdoor sport event. Our approach shows that a tweet-based analysis can enable social scientists to probe and survey public response to events such as air quality in a timely fashion and help policy makers respond appropriately.

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From Urban Air Quality Forecasting and Information Systems to Integrated Urban Hydrometeorology, Climate and Environment Systems and Services for Smart Cities

10.5194/egusphere-egu2020-8837 ◽

2020 ◽

Author(s):

Alexander Baklanov ◽

Keyword(s):

Climate Change ◽

Air Pollution ◽

Air Quality ◽

Urban Areas ◽

Urban Air Pollution ◽

Urban Air Quality ◽

Urban Air ◽

Climate Services ◽

Urban Weather ◽

Air Quality Forecasting

This presentation is analysing a modern evolution in research and development from specific urban air quality systems to multi-hazard and integrated urban weather, environment and climate systems and services and provides an overview of joint results of large EU FP FUMAPEX, MEGAPOLI, EuMetChem and MarcoPolo projects and international WMO GURME and IUS teams.&#160;Urban air pollution is still one of the key environmental issues for many cities around the world. A number of recent and previous international studies have been initiated to explore these issues. In particular relevant experience from several European projects will be demonstrated. MEGAPOLI studies aimed to assess the impacts of megacities and large air-pollution hotspots on local, regional and global air quality; to quantify feedback mechanisms linking megacity air quality, local and regional climates, and global climate change; and to develop improved tools for predicting air pollution levels in megacities (doi:10.5194/asr-4-115-2010). FUMAPEX developed for the first time an integrated system encompassing emissions, urban meteorology and population exposure for urban air pollution episode forecasting, the assessment of urban air quality and health effects, and for emergency preparedness issues for urban areas (UAQIFS: Urban Air Quality Forecasting and Information System; doi.org/10.5194/acp-6-2005-2006; doi.org/10.5194/acp-7-855-2007).While important advances have been made, new interdisciplinary research studies are needed to increase our understanding of the interactions between emissions, air quality, and regional and global climates. Studies need to address both basic and applied research and bridge the spatial and temporal scales connecting local emissions, air quality and weather with climate and global atmospheric chemistry. WMO has established the Global Atmosphere Watch (GAW) Urban Research Meteorology and Environment (GURME) project which provides an important research contribution to the integrated urban services.Most of the disasters affecting urban areas are of a hydro-meteorological nature and these have increased due to climate change. Cities are also responsible not only for air pollution emissions, but also for generating up to 70% of GHG emissions that drive large scale climate change. Thus, there is a strong feedback between contributions of cities to environmental health, climate change and the impacts of climate change on cities and these phases of the problem should not be considered separately. There is a critical need to consider the problem in a complex manner with interactions of climate change and disaster risk reduction for urban areas (doi:10.1016/j.atmosenv.2015.11.059, doi.org/10.1016/j.uclim.2017.05.004).WMO is promoting safe, healthy and resilient cities through the development of Integrated Urban Weather, Environment and Climate Services (IUS). The aim is to build urban services that meet the special needs of cities through a combination of dense observation networks, high-resolution forecasts, multi-hazard early warning systems, disaster management plans and climate services. This approach gives cities the tools they need to reduce emissions, build thriving and resilient communities and implement the UN Sustainable Development Goals. The Guidance on IUS, developed by a WMO inter-programme working group, documents and shares the good practices that will allow countries and cities to improve the resilience of urban areas to a great variety of natural and other hazards (https://library.wmo.int/doc_num.php?explnum_id=9903).

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Global Air quality Forecast and Information Systems (GAFIS) - a new WMO - GAW initiative

10.5194/egusphere-egu2020-20058 ◽

2020 ◽

Author(s):

Johannes Flemming ◽

Okasna Tarasova ◽

Lu Ren ◽

Alexander Baklanov ◽

Greg Carmichael

Keyword(s):

Air Pollution ◽

Information Systems ◽

Air Quality ◽

Weather Prediction ◽

Water Security ◽

Atmospheric Composition ◽

Spatial And Temporal Scales ◽

Air Quality Forecast ◽

The Impact ◽

Quality Forecast

Air pollution is the single largest environmental risk factor to health globally; it contributes to climate change, is detrimental for ecosystems, damages property, impacts visibility and can threaten food and water security. A wide variety of Air Quality (AQ) systems operate at different spatial and temporal scales to provide information required to mitigate the impact of or to reduce air pollution.&#160;Recognising the importance to support the transition of scientific efforts into useful services, the Global Atmosphere Watch Programme (GAW) of the World Meteorological Organisation (WMO) has started an initiative on Global Air quality Forecast and Information Systems (GAFIS). GAFIS aims to become a network for the development of good practices for air quality forecasting and monitoring services using &#160;diverse approaches. GAFIS will closely interact with existing GAW efforts on air pollution forecasting and dust strom prediction, and it intends to build strong links with the international health community. As a major first step, GAFIS will carry out and maintain a survey of AQ information systems and identify areas and regions with a lack of adequate AQ services. GAFIS aims to improve access to air quality observations and to encourage better quality control and meta-data provision.&#160; GAFIS will initiate coordinated evaluation activities of air quality services using a harmonized evaluation protocol. Finally, &#160;promoting operational applications of atmospheric composition feedbacks in Numerical Weather Prediction is a further objective of GAFIS.In the presentation we will introduce GAFIS to the scientific community and invite collaboration within its framework.&#160;

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A survey on the perceived need and value of decision-support tools for joint mitigation of air pollution and climate change in cities

Elem Sci Anth ◽

10.1525/elementa.126 ◽

2017 ◽

Vol 5 ◽

Author(s):

Erika von Schneidemesser ◽

Rebecca D. Kutzner ◽

Julia Schmale

Keyword(s):

Climate Change ◽

Air Pollution ◽

Decision Support ◽

Air Quality ◽

Regional Scale ◽

Decision Support Tool ◽

Mitigation Measures ◽

Decision Support Tools ◽

Support Tool ◽

Support Tools

Decision-support tools are increasingly popular for informing policy decisions linked to environmental issues. For example, a number of decision-support tools on transport planning provide information on expected effects of different measures (actions, policies, or interventions) on air quality, often combined with information on noise pollution or mitigation costs. These tools range in complexity and scale of applicability, from city to international, and include one or several polluting sectors. However, evaluation of the need and utility of tools to support decisions on such linked issues is often lacking, especially for tools intended to support local authorities at the city scale. Here we assessed the need for and value of combining air pollution and climate change mitigation measures into one decision-support tool and the existing policy context in which such a tool might be used. We developed a prototype decision-support tool for evaluating measures for coordinated management of air quality and climate change; and administered a survey in which respondents used the prototype to answer questions about demand for such tools and requirements to make them useful. Additionally, the survey asked questions about participants’ awareness of linkages between air pollution and climate change that are crucial for considering synergies and trade-offs among mitigation measures. Participants showed a high understanding of the linkages between air pollution and climate change, especially recognizing that emissions of greenhouse gases and air pollutants come from the same source. Survey participants were: European, predominantly German; employed across a range of governmental, non-governmental and research organizations; and responsible for a diversity of issues, primarily involving climate change, air pollution or environment. Survey results showed a lack of awareness of decision-support tools and little implementation or regular use. However, respondents expressed a general need for such tools while also recognizing barriers to their implementation, such as limited legal support or lack of time, finances, or manpower. The main barrier identified through this study is the mismatch between detailed information needed from such tools to make them useful at the local implementation scale and the coarser scale information readily available for developing such tools. Significant research efforts at the local scale would be needed to populate decision-support tools with salient mitigation alternatives at the location of implementation. Although global- or regional-scale information can motivate local action towards sustainability, effective on-the-ground implementation of coordinated measures requires knowledge of local circumstances and impacts, calling for active engagement of the local research communities.

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Scalable kernel-based SVM classification algorithm on imbalance air quality data for proficient healthcare

Complex & Intelligent Systems ◽

10.1007/s40747-021-00435-5 ◽

2021 ◽

Author(s):

Shwet Ketu ◽

Pramod Kumar Mishra

Keyword(s):

Air Pollution ◽

Air Quality ◽

Class Imbalance ◽

Imbalanced Data ◽

Classification Algorithm ◽

Quality Data ◽

Pollution Level ◽

Classification Problems ◽

Chi Square ◽

The Impact

AbstractIn the last decade, we have seen drastic changes in the air pollution level, which has become a critical environmental issue. It should be handled carefully towards making the solutions for proficient healthcare. Reducing the impact of air pollution on human health is possible only if the data is correctly classified. In numerous classification problems, we are facing the class imbalance issue. Learning from imbalanced data is always a challenging task for researchers, and from time to time, possible solutions have been developed by researchers. In this paper, we are focused on dealing with the imbalanced class distribution in a way that the classification algorithm will not compromise its performance. The proposed algorithm is based on the concept of the adjusting kernel scaling (AKS) method to deal with the multi-class imbalanced dataset. The kernel function's selection has been evaluated with the help of weighting criteria and the chi-square test. All the experimental evaluation has been performed on sensor-based Indian Central Pollution Control Board (CPCB) dataset. The proposed algorithm with the highest accuracy of 99.66% wins the race among all the classification algorithms i.e. Adaboost (59.72%), Multi-Layer Perceptron (95.71%), GaussianNB (80.87%), and SVM (96.92). The results of the proposed algorithm are also better than the existing literature methods. It is also clear from these results that our proposed algorithm is efficient for dealing with class imbalance problems along with enhanced performance. Thus, accurate classification of air quality through our proposed algorithm will be useful for improving the existing preventive policies and will also help in enhancing the capabilities of effective emergency response in the worst pollution situation.

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Abstract 36: Mapping and Multilevel Modeling of Climate Change and Air Pollution With Risk of Stroke in the United States and China: Findings From the Drexel-SARI Low Carbon and Healthy City Study

Stroke ◽

10.1161/str.47.suppl_1.36 ◽

2016 ◽

Vol 47 (suppl_1) ◽

Author(s):

Longjian Liu ◽

Hui Liu ◽

Xuan Yang ◽

Feng Jia ◽

Mingquan Wang

Keyword(s):

Climate Change ◽

Air Pollution ◽

Air Quality ◽

Pollution Index ◽

The United States ◽

Potential Interaction ◽

Low Carbon ◽

Multilevel Regression ◽

Healthy City ◽

Nationally Representative

Introduction and Hypothesis: Stroke is a leading cause of death and the major cause of disability in the world. However, few studies applied multilevel regression techniques to explore the association of stroke risk with climate change and air pollution. In the study, we aimed to test the hypothesis that the disproportionately distributed stroke rates across the counties and cities within a country are significantly associated with air pollution and temperature. Methods: We used data from U.S. 1118 counties in 49 states, which had estimated measures of particulate matter (PM)2.5 for the years 2010-2013, and data from China 120 cities in 32 provinces (including 4 municipalities), which had measures of Air Pollution Index (API) for the years 2012-2013. We assessed the association between air quality and prevalence of stroke using spatial mapping, autocorrelation and multilevel regression models. Results: Findings from the U.S. show that the highest average PM2.5 level was in July (10.2 μg/m3) and the lowest in October (7.63 μg/m3) for the years 2010-2013. Annual average PM2.5 levels were significantly different across the 1118 counties, and were significantly associated with stroke rates. Multilevel regression analysis indicated that the prevalence of stroke significantly increased by 1.19% for every 10 μg/m3 increase of PM2.5 (p<0.001). Significant variability in PM2.5 by states was observed (p=0.019). More than 70% of the variation in stroke rates existed across the counties (p=0.017) and 18.7% existed across the states (p=0.047). In China, the highest API was observed in the month of December, with a result of 75.76 in 2012 and 97.51 in 2013. The lowest API was observed in July, with a result of 51.21 in 2012, and 54.23 in 2013. Prevalence of stroke was significantly higher in cities with higher API concentrations. The associations between air quality and risk of stroke were significantly mediated by temperatures. Conclusions: The study, using nationally representative data, is one of the first studies to address a positive and complex association between air quality and prevalence of stroke, and a potential interaction effect of temperatures on the air - stroke association.

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CAMS-Net: The Clean Air Monitoring and Solutions Network

10.5194/egusphere-egu21-13912 ◽

2021 ◽

Author(s):

Daniel Westervelt ◽

Celeste McFarlane ◽

Faye McNeill ◽

R (Subu) Subramanian ◽

Mike Giordano ◽

...

Keyword(s):

Air Pollution ◽

Air Quality ◽

Linear Regression ◽

Multiple Linear Regression ◽

Low Cost ◽

Air Monitoring ◽

Quality Data ◽

Calibration Methods ◽

The World ◽

Clean Air

There is a severe lack of air pollution data around the world. This includes large portions of low- and middle-income countries (LMICs), as well as rural areas of wealthier nations as monitors tend to be located in large metropolises. Low cost sensors (LCS) for measuring air pollution and identifying sources offer a possible path forward to remedy the lack of data, though significant knowledge gaps and caveats remain regarding the accurate application and interpretation of such devices.The Clean Air Monitoring and Solutions Network (CAMS-Net) establishes an international network of networks that unites scientists, decision-makers, city administrators, citizen groups, the private sector, and other local stakeholders in co-developing new methods and best practices for real-time air quality data collection, data sharing, and solutions for air quality improvements. CAMS-Net brings together at least 32 multidisciplinary member networks from North America, Europe, Africa, and India. The project establishes a mechanism for international collaboration, builds technical capacity, shares knowledge, and trains the next generation of air quality practitioners and advocates, including domestic and international graduate students and postdoctoral researchers.&#160;Here we present some preliminary research accelerated through the CAMS-Net project. Specifically, we present LCS calibration methodology for several co-locations in LMICs (Accra, Ghana; Kampala, Uganda; Nairobi, Kenya; Addis Ababa, Ethiopia; and Kolkata, India), in which reference BAM-1020 PM2.5 monitors were placed side-by-side with LCS. We demonstrate that both simple multiple linear regression calibration methods for bias-correcting LCS and more complex machine learning methods can reduce bias in LCS to close to zero, while increasing correlation. For example, in Kampala, Raw PurpleAir PM2.5 data are strongly correlated with the BAM-1020 PM2.5 (r2 = 0.88), but have a mean bias of approximately 12 &#956;g m-3. Two calibration models, multiple linear regression and a random forest approach, decrease mean bias from 12 &#956;g m-3 to -1.84 &#181;g m-3 or less and improve the the r2 from 0.88 to 0.96. We find similar performance in several other regions of the world. Location-specific calibration of low-cost sensors is necessary in order to obtain useful data, since sensor performance is closely tied to environmental conditions such as relative humidity. This work is a first step towards developing a database of region-specific correction factors for low cost sensors, which are exploding in popularity globally and have the potential to close the air pollution data gap especially in resource-limited countries.&#160;&#160;&#160;

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Clean air in the Anthropocene

Faraday Discussions ◽

10.1039/c7fd90032e ◽

2017 ◽

Vol 200 ◽

pp. 693-703 ◽

Cited By ~ 18

Author(s):

Jos Lelieveld

Keyword(s):

Air Pollution ◽

Sustainable Development ◽

Air Quality ◽

Human Health ◽

Atmospheric Chemistry ◽

Clean Energy ◽

Atmospheric Composition ◽

Anthropogenic Sources ◽

Years Of Life Lost ◽

Clean Air

In atmospheric chemistry, interactions between air pollution, the biosphere and human health, often through reaction mixtures from both natural and anthropogenic sources, are of growing interest. Massive pollution emissions in the Anthropocene have transformed atmospheric composition to the extent that biogeochemical cycles, air quality and climate have changed globally and partly profoundly. It is estimated that mortality attributable to outdoor air pollution amounts to 4.33 million individuals per year, associated with 123 million years of life lost. Worldwide, air pollution is the major environmental risk factor to human health, and strict air quality standards have the potential to strongly reduce morbidity and mortality. Preserving clean air should be considered a human right, and is fundamental to many sustainable development goals of the United Nations, such as good health, climate action, sustainable cities, clean energy, and protecting life on land and in the water. It would be appropriate to adopt “clean air” as a sustainable development goal.

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Air Pollution, Air Quality, and Climate Change

Heat Advisory ◽

10.7551/mitpress/9780262034876.003.0007 ◽

2016 ◽

Author(s):

Alan H. Lockwood

Keyword(s):

Climate Change ◽

Air Pollution ◽

Carbon Monoxide ◽

Air Quality ◽

Quality Standards ◽

Ambient Air ◽

Ozone Production ◽

Atmospheric Water ◽

Particulate Pollution ◽

Volatile Organic

The effects of climate change on air quality are difficult to model due to the large number of unpredictable variables. Hotter temperatures favor ozone production. Higher atmospheric water content may blunt this effect in some regions. Higher levels of natural volatile organic compounds (VOCs), such as terpenes from plants, are likely to act synergistically with anthropogenic VOCs to favor ozone production. Droughts increase wildfire risks that produce particulate pollution and carbon monoxide, a VOC involved in ozone production. Some models predict increased ozone concentrations in many urban settings. Future revisions of National Ambient Air Quality Standards, a process driven by politics and science, should consider these effects.

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EURODELTA-Trends, a multi-model experiment of air quality hindcast in Europe over 1990–2010

Geoscientific Model Development ◽

10.5194/gmd-10-3255-2017 ◽

2017 ◽

Vol 10 (9) ◽

pp. 3255-3276 ◽

Cited By ~ 19

Author(s):

Augustin Colette ◽

Camilla Andersson ◽

Astrid Manders ◽

Kathleen Mar ◽

Mihaela Mircea ◽

...

Keyword(s):

Air Pollution ◽

Air Quality ◽

Boundary Conditions ◽

Radiative Forcing ◽

Regional Scale ◽

Air Pollutant ◽

Pollutant Emissions ◽

Mitigation Measures ◽

Modelling Experiment ◽

Technical Features

Abstract. The EURODELTA-Trends multi-model chemistry-transport experiment has been designed to facilitate a better understanding of the evolution of air pollution and its drivers for the period 1990–2010 in Europe. The main objective of the experiment is to assess the efficiency of air pollutant emissions mitigation measures in improving regional-scale air quality. The present paper formulates the main scientific questions and policy issues being addressed by the EURODELTA-Trends modelling experiment with an emphasis on how the design and technical features of the modelling experiment answer these questions. The experiment is designed in three tiers, with increasing degrees of computational demand in order to facilitate the participation of as many modelling teams as possible. The basic experiment consists of simulations for the years 1990, 2000, and 2010. Sensitivity analysis for the same three years using various combinations of (i) anthropogenic emissions, (ii) chemical boundary conditions, and (iii) meteorology complements it. The most demanding tier consists of two complete time series from 1990 to 2010, simulated using either time-varying emissions for corresponding years or constant emissions. Eight chemistry-transport models have contributed with calculation results to at least one experiment tier, and five models have – to date – completed the full set of simulations (and 21-year trend calculations have been performed by four models). The modelling results are publicly available for further use by the scientific community. The main expected outcomes are (i) an evaluation of the models' performances for the three reference years, (ii) an evaluation of the skill of the models in capturing observed air pollution trends for the 1990–2010 time period, (iii) attribution analyses of the respective role of driving factors (e.g. emissions, boundary conditions, meteorology), (iv) a dataset based on a multi-model approach, to provide more robust model results for use in impact studies related to human health, ecosystem, and radiative forcing.

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