Introduction

Agenda

• The basics of air quality


• Primary and secondary air pollutants


• Sources and emissions of air pollutants in regional, municipal and higher spatial scales


• Links of air pollution with human health, ecosystem and urban climate


• Air quality monitoring using ground-based and satellite instruments


• Air quality modeling tools


• Air quality and Smart Cities

Learning outcomes

• Identidy air pollution and its effects


• Understand the harmful pollutants and their effects on human health, ecosystem and urban environment


• Recognize the major emission sources of air pollutants in a specific region


• Correlate effects of meteorology on air pollution


• Designate the role of air quality monitoring and modeling for emission reductions


• Interpret EO-derived air quality maps

The Space/Geospatial Sector Skills Alliance

Towards an innovative strategy for skills development and capacity building in the space geo-information sector supporting Copernicus User Uptake

The EO4GEO project

• Duration: 4 years from January the 1st, 2018
• Budget: 3,87 million €
• Partnership: 25 Partners + 30+ Associated Partners (from 16 EU Countries) from Academia, Companies and networks
• Coordinators: GISIG (General), KU Leuven (Scientific & Technological), PLUS (Education & Training), Climate-KIC (Exploitation)

The VISION

To foster the growth of the European Earth Observation / Geographic Information (EO/GI) sector ensuring a workforce with the right skills, in the right place, at the right time.

The MISSION

To ensure the strategic cooperation among stakeholders on skills development in the EO/GI sector.

EO4GEO IS MUCH MORE

• A series of pre-defined curiculla in support of Copernicus


• A portfolio of training modules directly usable in the context of Copernicus and other relevant prograns


• A series of training actions (webinars, academic courses, etc.) in the three sub-sectors - integrated applications, smart cities and climate change


• A mobility program to promote internships and on-the-job training


• A Long-term Action plan to sustain the proposed solutions

Air Quality Monitoring and Management

Let's start from the basics...

The Basics of Air Pollution

Air Pollution

EPA (US Environmental Agency):

The presence of contaminants or pollutant substances in the air that interfere with human health or welfare, or produce other harmful environmental effects.

Other definition:

The atmospheric condition where air pollutants are present in concentrations that are a concern, or even an immediate danger for human health, ecosystems or infrastructure.

Air Pollutants

The presence of contaminants or pollutant substances in the air that interfere with human health or welfare, or produce other harmful environmental effects.

The basics of air quality

Air Quality

Refers to the degree to which the air is suitable for humans and the environment.

Rules of three for survive (Source: Wikipedia)

• A human can survive three weeks without food
• A human can survive three days without drinkable water
• A human can survive three hours in extreme cold or heat
• A human can survive three minutes without breathable air

Breathable Air is the first requirement

In terms of Air Quality: Survival time differs depending on the type and concentration of the air pollutant and the exposure of the receptor.

The cycle of air pollution

EEA (Environmental Protection Agency)

Ask people what they regard as the defining characteristic of air pollution Refer to the degraded air quality Know that something amiss because of: Reduced visibility Unpleasant odor Health impacts

Sources of Air Pollution

EEA (Environmental Protection Agency)

Man-made or anthropogenic sources: Agricultural activities Energy production Waste, coal mining Transportation Fuel combustion (businesses, public buildings, households) Natural sources Volcanoes, dust storms, sea-spray

Atmospheric Pollutants

ESA (European Space Agency) Europe: Forecast for 25/05 12:00 UTC (Copernicus website)

Carbon Monoxide (CO) Characteristics: Colorless, Odorless, Tasteless, Non-corrosive, highly poisonus, very flammable Emission sources Combustion Climate impacts Contribution to the production of O3 and CO2

Atmospheric Pollutants

ESA (European Space Agency) Europe: Forecast for 25/05 12:00 UTC (Copernicus website)

Nitrogen Oxides (NOx) Characteristics: NO: Colorless, toxic gas NO2: yellow, reddish-brown in high concentrations, toxic gas Emission sources Fuel combustion Soils Climate impacts Production of O3, eutrophication (aquatic ecosystems), fertilization

Atmospheric Pollutants

Kilauea volcano eruptions in Hawaii (ESA - European Spase Agency) Europe: Forecast for 25/05 12:00 UTC (Copernicus website)

Sulphur Dioxide (SO2) Characteristics: Colorless, odorful at high concentrations, soluble Emission sources Volcanoes Combustion of coal, diesel, fuel oil Industrial processing Climate impacts Contribution to the formation of ‘acid rain’ and particulate matter

Atmospheric Pollutants

South Pole: Forecast for 25/05 12:00 UTC (Copernicus website) Europe: Forecast for 25/05 12:00 UTC (Copernicus website)

Ozone (O3) Characteristics: Light blue gas, odorful, irritating, toxic, explosive, not released directly into the atmosphere, strongly oxidizing Emission sources Secondary pollutant formed in urban areas primarily from VOCs and NOx Climate impacts Reduced primary productivity, changes in radiative budget

Atmospheric Pollutants

Particulate Matter (PMx) Characteristics: Solid or liquid atmospheric air substances Heterogeneous in shape, size and chemical composition Emission sources Combustion Mechanical abrassion Formation from condensation of gas-phase species, sea-salt and desert dust Climate impacts Changes in the radiative transfer Reduction of visibility Variation of albedo

Scales of Air Pollution

Local scale: Small number of large emitters Local scale: Several number of small emitters

Urban scale: Urban metabolism (Oke, 2017) Urban scale: Photochemical smog

Air Pollution & Human Health

Air Pollution & Ecosystems

Acid Rain effects – Sources: Wikipedia (left) and USGS (right) International Atomic Energy Agency (IAEA)

Air pollutants adversely affect downwind aquatic and terrestrial ecosystems, and structures Water and soil acidification lake eutrophication, fertilization and damage to agro- and forest ecosystems

The European Air Quality Index

• Allows users to understand more about air quality where they live, work or travel
• The Index is based on concentration values for five key pollutants (particulate matter (PM₁₀, PM_2.5), ozone (O₃), nitrogen dioxide (NO₂) and sulphur dioxide (SO₂))

The PM_2.5 network at Patras

The reason

• Very small number of stations, unable to cover the spatial and temporal variability of PM
• The concern of citizens

The funding scheme

• Crowd & public funding

The basic issue

• Use of (low cost but not cheap) IoT sensors
• Provide information in real-time & 24/7

The added value

You cannot manage if you do not measure

The PM_2.5 network at Patras

Presentation of Results: Maps

The PM_2.5 network at Patras

Presentation of Results: Graphs for the last 24 hours/7days

The PM_2.5 network at Patras

Presentation of Results: The distribution of stations

The PM_2.5 network at Patras

Presentation of Results: The monhtly distribution of PM_2.5

The PM_2.5 network at Patras

Presentation of Results: The monhtly distribution of PM_2.5

The PM_2.5 network at Patras

Presentation of Results: The daily distribution of PM_2.5 (Winter)

Copernicus atmosphere monitoring service

What is Copernicus

• Copernicus is an EU programme aimed at developing European information services based on satellite Earth observation and in-situ (non-space) data.
• Copernicus is implemented by the European Comission with the support from the European Space Agency (ESA) for the Space component and the European Environment Agency (EEA) for the in-situ component.

Copernicus atmosphere monitoring service

The objective of Copernicus

• To monitor and forecast the state of the environment on land, sea and in the atmosphere, in order to support climate change mitigation and adaptation strategies, the efficient management of emergency situations and the improvement of the security of every citizen.

  https://emergency.copernicus.eu/mapping/ems/what-copernicus

• It combines satellite observation data with data from sensor networks on the Earth’s surface to build a comprehensive picture of our planet and its environment.

  https://www.eea.europa.eu/about-us/who/copernicus-1

Copernicus atmosphere monitoring service

What does the service do?

The service adds value to observations, providing consistent information on the atmosphere anywhere in the world, allowing you to acess the past and to predict the next few days.

At the core of the service is direct acess to reliable data and expertise related to air quality, solar energy, and the role atmospheric gases and particles play in climate change.

The services use satellite and ground-based observations with forecast models to support businesses, policy makers and scientists dealing with the challenges and opportunities related to the composition of the atmosphere.

https://https://atmosphere.copernicus.eu/what-we-do

Copernicus atmosphere monitoring service

CAMS is a collaboration of:

• European Centre for Medium-Range Weather Forecasts (ECMWF)
• European Space Agency (ESA)
• European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)
• Many other organisations providing satellite and in-situ observations

Modeling the fate of atmospheric pollutants

Two approaches Deterministic Data Driven

Modeling the fate of atmospheric pollutants

Chemical Weather Models Deterministic,based on mathematical equations that represent the dynamical, chemical and physical processes of the atmosphere in relation to pollutants. Driven by meteorology and emissions, site and surrounding conditions Simulate the chemical transformation and the dispersion, transport and removal of pollutants Extract pollutant concentrations at time steps within grid cells at a user-specified domain

Modeling the fate of atmospheric pollutants

Data Driven Models Seek input-output relationships in a historical database Require training prior to forecasting on the basis of past monitoring (air quality) data. Provide forecast at the location of the monitoring stations. Stand-alone models or as tools to improve the skill of deterministic air quality models

Modeling the fate of atmospheric pollutants

Analog Ensemble (AnEn) Uses time-series of past predictions by an air quality model and their corresponding observations Focuses on the current deterministic forecast Selects the most similar historical forecasts to the current air quality forecast Uses the mean value of their corresponding observations as the current AnEn forecast

Modeling the fate of atmospheric pollutants

Long Short Term Memory network (LSTM) Has hidden layers and internal memory, which preserve the long-term information Has the structure of a chain of repeating modules Connects previous information with the current Considers the past conditions to make the current forecast

Measuring air pollutants

Patras Air: Ground-based network for PM2.5 in Patras

Low-Cost Sensors Low-cost sensors are used to monitor air quality in cities Low-cost sensors gain popularity for: Potential to be installed in dense distribution Affordable operational cost Calibrating them with the appropriate techniques can generate reliable data in increased spatial coverage.

A case study in Patras

Ground-based network for PM2.5 in Patras

Low-Cost Sensors Patras: urban coastal Mediterranean city in western Greece Characteristics: one of the largest ports in Greece, mediterranean climate Anthropogenic sources: traffic (road, maritime), indoor activities (wood burning, cooking, etc.) Patras Air is a dense network of low-cost sensors measuring air pollution.

A case study in Patras

Through a hybrid-scheme utilizing deterministic & data-driven models with a dense monitoring network, we could obtain PM_2.5 and PM₁₀ forecasts at fine scale

A case study in Patras

PM_2.5, PM₁₀ (CAMS)

A case study in Patras

PM_2.5, PM₁₀ (CAMS & PatrasAir)

A case study in Patras

PM_2.5 & PM₁₀ (CAMS, PatrasAir & Models)

A case study in Patras

Spatial distribution of PM_2.5 & PM₁₀: Feb 2019 00:00 UTC

A case study in Patras

Spatial distribution of PM_2.5 & PM₁₀: Feb 2019 06:00 UTC

A case study in Patras

Spatial distribution of PM_2.5 & PM₁₀: Feb 2019 12:00 UTC

A case study in Patras

Spatial distribution of PM_2.5 & PM₁₀: Feb 2019 18:00 UTC