health impact assessment of air pollution: introductory

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Abstract

AirQ+ is a software tool for quantifying the health burden and impact of air pollution developed by the WHO Regional Office for Europe. AirQ+ includes methodologies to assess the impacts of short- and long-term exposure to ambient air pollution. The main methodologies use evidence generated by epidemiological cohort studies showing a relationship between average long-term air pollution concentration levels and the mortality risks in exposed populations. Assessing the impact of air pollution is suggested when evaluating the consequences of policies and interventions or of hypothetical scenarios. AirQ+ should always be used with the support of an epidemiologist or air pollution impact assessment expert. To facilitate users in their analyses, AirQ+ comes with manuals that require increasing levels of expertise. This manual shows how to install, run and perform simple analyses with AirQ+ to introduce users to some of the software’s features. For example, users can calculate the attributable proportion of adverse health impacts estimated to occur due to exposure to a specific level of air pollution in a given population for a certain time period. This is used to estimate the number of attributable cases due to air pollution.

Keywords

AIR POLLUTIONEXPOSUREHEALTH IMPACTS

Document number: WHO/EURO:2020-1557-41308-56210

Contents

Acknowledgments ................................................................................................................................... iv

Introduction............................................................................................................................................... 1

Installing AirQ+ ........................................................................................................................................ 2

Starting AirQ+ .......................................................................................................................................... 3Colour-coded data entry fields ...................................................................................................................................... 4

Test input datasets.................................................................................................................................. 6CityData datasets ................................................................................................................................................................. 6

Country Life Table dataset ............................................................................................................................................... 7

Multiple-area datasets ....................................................................................................................................................... 8

Example CityData analysis: ambient air pollution – PM2.5 – long-term – adult mortality ................................................................................................ 9

Data input: yearly mean PM2.5 value............................................................................................................................ 9

Data input: PM2.5 daily frequency data ...................................................................................................................... 13

Example CityData analysis: ambient air pollution – PM2.5 – short-term – adult mortality .............................................................................................. 16

Example CityData analysis: ambient air pollution – PM2.5 – long-term – adult mortality – use of IER ......................................................................... 19

Data input: two PM2.5 yearly average values .......................................................................................................... 19

Example CityData analysis: ambient air pollution – PM2.5 – long-term – adult mortality – use of interim targets values ...................................... 21

Data input: PM2.5 yearly average ................................................................................................................................... 21

Example CityData analysis: ambient air pollution – ozone – long-term – adult mortality ............................................................................................... 22

Data input: SOMO35 ........................................................................................................................................................... 22

Data input: daily ozone data ............................................................................................................................................ 23

Health impact assessment of air pollution: introductory manual to AirQ+iv

Acknowledgments

The authors of this publication are: Pierpaolo Mudu (European Centre for Environment and Health, WHO Regional Office for Europe), Michal Krzyzanowski (Kings College London, United Kingdom of Great Britain and Northern Ireland) and Christian Gapp (WHO Regional Office for Europe). The WHO Regional Office for Europe gratefully acknowledges Brian Miller (Institute of Occupational Medicine, United Kingdom of Great Britain and Northern Ireland), Sophie Gumy (WHO headquarters), Heresh Amini (University of Copenhagen, Denmark), Joseph Spadaro (Spadaro Environmental Research Consultants, Philadelphia, Pennsylvania, United States of America), Ingu Kim (WHO European Centre for Environment and Health, WHO Regional Office for Europe), Magali Corso (Sante publique France), Alain Le Tertre (Sante publique France), Sylvia Medina (Sante publique France) and Myriam Tobollik (German Environment Agency) for their comments and suggestions to the authors. The AirQ+ project was partially financed by the German Federal Ministry for the Environment, Nature Conservation and Nuclear Safety.

This manual is partially based on the publication:

Mudu P, Gapp C, Dunbar M (2018). AirQ+ – example of calculations. Copenhagen: WHO Regional Office for Europe.

Health impact assessment of air pollution: introductory manual to AirQ+ 1

Introduction

AirQ+ is a software tool for quantifying the health burden and impact of air pollution developed by the WHO Regional Office for Europe. AirQ+ includes in a user-friendly way methodologies to assess the effects of long-term and short-term exposure to ambient air pollution.1 It works with the following pollutants: particulate matter with a diameter of 2.5 µm or less (PM2.5) or with a diameter of 10 µm or less (PM10), nitrogen dioxide, ozone and black carbon. AirQ+ can estimate the effects of household air pollution related to solid fuel use. Various health outcomes related to mortality and morbidity, both in terms of acute and chronic conditions can be considered in the calculations.

AirQ+ can be used to estimate the risk of cancer due to lifetime exposure to a carcinogenic air pollutant. The software uses default values based on the scientific evidence on health effects from ambient air pollution that mainly comes from studies conducted in western Europe and North America. When conducting assessments outside these regions, the results should be carefully considered and may have a higher degree of uncertainty; the user should consider seeking expert judgement.

This version of AirQ+ is structured in three modules that allow the quantification of health impact assessment related to (i) the achievement of specific air pollution targets or changes in pollution levels; (ii) the burden of disease approach using the integrated exposure–response (IER) functions; and (iii) risk analysis for carcinogenic air pollutants using unit risk. To facilitate users in their analyses, the software comes with five manuals:

1. Health impact assessment of air pollution: introductory manual to AirQ+2. Health impact assessment of air pollution: AirQ+ multiple-area data input 3. Health impact assessment of air pollution: AirQ+ life table manual4. AirQ+ burden of disease due to air pollution manual5. AirQ+ carcinogenic pollutants and risk analysis.

All manuals are available online2 and are accessible from the AirQ+ welcome window (select the Manuals button in the upper-right corner). This manual provides basic information on how to install the software, run AirQ+ and perform simple analyses so the user becomes familiar with some of the software’s features. Additional files are available online,3 such as an excel data collection template. AirQ+ also comes with help texts and documents, such as the Glossary.2 It is recommended to always use AirQ+ with the support of an epidemiologist or air pollution impact assessment expert.

1 For details on key terms please refer to the AirQ+ Glossary, which is accessible on the main welcome window.2 WHO Regional Office for Europe (2019). AirQ+: software tool for health risk assessment of air pollution [website]. Copenhagen:

http://www.euro.who.int/en/health-topics/environment-and-health/air-quality/activities/airq-software-tool-for-health-risk-assessment-of-air-pollution, accessed 21 November 2019).

Health impact assessment of air pollution: introductory manual to AirQ+2

Installing AirQ+

It is recommended to create a dedicated folder for AirQ+ on your local hard drive. Download AirQ+ as a compressed (zip) file to the folder and extract the files. Do not change the location of files or filenames. Start the program by double-clicking “AirQPlus.exe”. AirQ+ can also run from an external data storage device such as a USB flash drive.

AirQ+ defaults to the English version when starting. To work in other languages than English, for example French or Russian, select the desired language in the drop-down box in the upper-right corner of the welcome window. Close and restart AirQ+ to begin working in the new language.

PrerequisitesAirQ+ is a standalone application using Java technologies; therefore, Java needs to be installed on the target computer. The program has been tested and runs on Windows 7, Windows 10, Linux/Ubuntu 18, Linux/Debian 9 and Macintosh/macOS Catalina (10.15). Since only a limited number of configurations could be tested, WHO declines all responsibility for errors, omissions or deficiencies regarding the use/maintenance of the tool and the accompanying documentation. For more information, click the Disclaimer button in the upper-right corner of the welcome window.

Dedicated foldersThe root AirQ+ folder has three subfolders: “dist”, “resources” and “testData”. The “dist” and “resources” folders and their contents must not be moved, deleted or renamed. The “testData” folder contains the data files used in the examples in the AirQ+ manuals.

Number formatsAirQ+ processes and stores numerical data using decimal points, even if the language and number format settings of the target machine are different. Comma-separated values (csv) files that use the semicolon (;) as the separator character can be used for data input and output. Since commas are used as decimal separators in many languages, this can lead to confusion. AirQ+ always uses the semicolon (;) as the separator character.

Valid input data for AirQ+ is 7.5; 8.002; 17.3 which indicates the three numbers: 7.5, 8.002 and 17.3.

Invalid input data are:

� 7.5,8.002,17.3 (uses commas as separator characters)

� 7,5; 8,002; 17,3 (uses commas as decimal separators).

The procedure for defining the semicolon as the separator character depends on the operating system of the target machine. Please consult the respective system’s help information. In Windows 10, for example, the separator character is globally defined in Control Panel – Clock and Region – Region – Additional settings – List separator.3

3 In Windows 7, for example, the separator character is defined in Control Panel – Region and Language – Formats – Additional settings – List separator.


Starting AirQ+

AirQ+ was developed with a user-friendly interface. Before performing a proper analysis, it is recommended to become familiar with the various functions using the sample data supplied. As a standalone program, AirQ+ does not need or establish Internet connections. Data and results are automatically saved and presented in a project tree for ease of management.

When the program is started, the welcome window is displayed (Fig. 1). The upper-left side of the window shows the (empty) project tree for short-term and long-term analyses. Next to the Projects Overview are six icons (from left to right) for managing analyses: add, delete, copy, export, compare and filter (Fig. 2).

Note: AirQ+ automatically saves projects; users do not need to save them.

Fig. 1. AirQ+ welcome window

Fig. 2. AirQ+ toolbar for managing projects

The version of AirQ+ is displayed at the lower-right corner of the welcome window or by clicking the information icon in the upper-right corner (Fig. 1). Select the Disclaimer button (next to the information button) to view and carefully read the disclaimer.


Colour-coded data entry fields

Data entry fields are colour-coded to help the user distinguish, for example, between mandatory and optional data.

Green indicates mandatory fields. Mandatory fields must be filled for AirQ+ computations. When a new analysis is created, mandatory fields contain correct default data. For example, the concentration mean value is initially set to zero. Green also indicates correct values were entered in mandatory and optional fields.

Yellow indicates suggested fields. It is strongly recommended to enter data in these fields for documentation purposes even though these fields are not used in calculations or analyses.

White indicates optional fields in the Analysis Properties window. Fields are always white in tables with measurement data in the Air Quality Data window. AirQ+ performs some data checking, depending on the type of field. For example, entering a negative value into the “Number of days” field will not be accepted.

Red indicates that an incorrect value was entered in a mandatory field. For example, concentration mean values cannot be negative.

The AirQ+ welcome window shows the three modules and the question each one answers.

� Create new Impact Assessment

� What would be the change in health if air pollution level decreases or increases?

� Create new Burden of Disease

� How much of a particular health outcome (e.g. mortality) is attributable to current exposure to an air pollutant?

� Create new Risk Analysis

� What is the risk of cancer associated with lifetime exposure to selected air pollutants for which unit risk is available?

Click the question mark next to each type of analysis for additional information.

The Impact Assessment module allows the user to develop two types of analyses. The first analysis is based on a single number for a population, air pollution data, health data and relative risk. To run this analysis, click the “Create new Impact Evaluation” button on the bottom of the Create New Analysis window and select “Impact Evaluation” from the drop-down box “Evaluation (optional)” (Fig. 3). The second analysis is based on population data that include for each age range the number of people and the number of deaths, plus air pollution data, health data and relative risk. To run this analysis, select “Life Table Evaluation” from the drop-down box “Evaluation (optional)” of the Create New Analysis window (Fig. 3).


Fig. 3. AirQ+ Create New Analysis: New Impact Assessment window

The Burden of Disease module includes the IER functions developed over the years in order to produce burden estimates based on cause-specific mortality data. This module also includes the relative risks for rough estimates of the burden of household air pollution. For more information, see the AirQ+ burden of disease of air pollution manual.

The Risk Analysis module is used to answer the question: in the case of absence of classical air pollutants data and the presence of other specific substances concentrations in the air, is it possible to quantify the risk of developing cancer? The results of a risk analysis do not replace the results of an impact assessment or burden of air pollution on a population. For more information, see the AirQ+ carcinogenic air pollutants manual.


Test input datasets

AirQ+ comes with test input data comprised of real and hypothetical data from a European city and a country. In order to become familiar with the sample data, it is highly recommended to study the content of the files using standard text editors like Gedit for Ubuntu or the Notepad utility in Windows. Make sure to always keep backup copies of the original files.

CityData datasets

These two files contain data from a European city (called CityData) and are used in the examples in this manual.

1. CityData_PM10_minmaxdays.csv

This file consists of sample PM10 data for an impact analysis using a frequency distribution for a period of one year.

Line 1 describes the names of the fields.Lines 2–19 contain the data values.

Example data are:

� line 1: min;max;days

� line 8: 60;70;23.

This means that in the 23-day period, average daily PM10 concentrations were between 60 and 70 µg/m3. For each data input interval, AirQ+ computes and uses the mean value for further calculations, i.e. 65 µg/m3.

2. CityData_daily_2004-2006.csv

This file consists of sample PM10 data for an impact analysis using daily mean concentrations for a period of three years.


Example data are:

� line 1: Date;Daily Mean PM10;Estimated Daily Mean PM2.5 (=0.58*PM10);Daily 8h-max Ozone; and

� line 10: 09/01/2004;87.8;50.9;44.9.

This means that on 9 January 2004, the daily average PM10 concentration was 87.8 µg/m3; the daily average PM2.5 concentration was 50.9 µg/m3 and the daily eight-hour-maximum concentration for ozone was 44.9 µg/m3.


Note: the date field is a text field that is only checked for duplicates; AirQ+ ignores duplicate date records, even if concentration values are different. The format of the date field is not relevant. “9 April 2004” would work as well as “2004 April 9”. Specific dates are not needed for AirQ+ computations.

The first line of an input file is mandatory and used by the AirQ+ input module, but the content is quite flexible. For example, the first line “Minimum PM10;Maximum PM10;Number of Days” would work as well as “min;max;days

Country Life Table dataset

The file “CountryLifeTable.csv” contains one year of data from a European country that is used to make life table calculations. For examples using this data file and other information, see the AirQ+ life table manual.

The data include:

� population (total population, five-year age groups from 0 to 99 years and the age group 100 years and over); and

� health data (all natural causes of mortality – all ages).

The file consists of population and mortality data by age range for one year.


Example data are:

� line 1: from;to end;mid year;death

� line 8: 30;34;4234400;3344.

This means that the age group 30–34 spans five years, includes people between 30 years of age and below 35, the mid-year population for the 30–34 age group is 4 234 400 and that age group had 3344 deaths in that year.

Note: a 35-year-old person belongs to the next age group (35–39 years).

The first line of an input file is mandatory and used by AirQ+ for its input module, but the content is quite flexible. For example, the first line “From age;Including age;Mid-year population, Number of deaths” would work as well.


Multiple-area datasets

AirQ+ comes with five data files to test the input of data from multiple areas (Table 1). For examples using these data files and other information, see the AirQ+ multi-data input manual.

Table 1. AirQ+ files to input data from multiple areas

File name Description

02AreasTestPMDailyValues.csv Contains daily mean PM2.5 concentration data from two areas, Subregion 1 and Subregion 2, for three years from 2004 to 2006

03AreasTestPMMinMaxDaysValues.csv Contains air quality data for PM2.5 between 10 and 190 µg/m3 from three areas: Subregion 1, Subregion 2 and Subregion 3; air pollution data include frequency of days within a specific range of PM2.5 values, step 10.4

12AreasTestPMDailyValues.csv Contains daily PM2.5 concentration data from 12 areas

13AreasTestPMYearlyMeans.csv Contains data from 13 areas with different yearly PM2.5 concentration mean values, ranging from 12 µg/m3 to 40 µg/m3

647Areas_with Incidence and Population.csv Contains data from 647 areas with different yearly PM2.5 concentration mean values, ranging from 2.74 µg/m3 to 16.37 µg/m3; data are modelled with populations ranging from 1 to 24 408, with different mortality rates.

4 The category 0–10 includes all air pollution concentration values equal to or greater than 0 and less than 10 [x, x+10[.


Example CityData analysis:

ambient air pollution – PM2.5 – long-term – adult mortality

Question to be addressed: How many deaths (out of the total number of deaths from natural causes) are attributable to long-term exposure to PM2.5 exceeding the WHO air quality guidelines (AQG)5 level (10 µg/m3)?

This example uses the “CityData_daily_2004-2006.csv “file. The monitoring results from 2004 to 2006 show an average PM2.5 concentration of 27.95 µg/m3, which is considered here as an indicator of long-term exposure of CityData residents. The user can answer this question with two types of air pollution data: (i) the yearly mean PM2.5 value; and (ii) frequency distribution data for air pollution. In the second case it is important that the user checks whether the amount of available data reasonably represents the period considered.

Data input: yearly mean PM2.5 value

In the New Impact Assessment window (Fig. 4), select ambient pollution, long-term effects and the pollutant PM2.5 and click OK.

Fig. 4. AirQ+ New Impact Assessment window for ambient air pollution – PM2.5 – long-term adult mortality

Only the mean value is needed for this straightforward, simple assessment. In the Analysis Properties window, select the Input Mean Value option and enter 27.95 µg/m3 into the Mean Value field. Enter “1690109” into the

5 WHO Regional Office for Europe (2006). Air quality guidelines: global update 2005: particulate matter, ozone, nitrogen dioxide, and sulfur dioxide. Copenhagen: WHO Regional Office for Europe (http://www.euro.who.int/en/health-topics/environment-and-health/air-quality/publications/pre2009/air-quality-guidelines.-global-update-2005.-particulate-matter,-ozone,-nitrogen-dioxide-and-sulfur-dioxide, accessed 12 November 2019).


Total Population field (Fig. 5). AirQ+ does not use the total population for the analysis, so the user can consider this as additional descriptive information.6

Note: in most cases the mean value (average) would be an annual mean. AirQ+ does not require that mean values are annual averages and allows, for example, three-year averages, six-month (seasonal) averages, etc.

Fig. 5. AirQ+ Analysis Properties window for ambient air pollution – PM2.5 – long-term adult mortality

Click the “Create new Impact Evaluation” button (Fig. 5) to go to the Impact Evaluation window (Fig. 6) and then enter the following data:

� evaluation name (for example: AAP7 PM2.5 long-term adult mortality);

� mortality incidence for adults (≥ 30 years), all natural causes, per 100 000 population: 939.73;

� the total number of adults (≥ 30 years) exposed to the pollutant: 1156588; the user can either enter a number that is less than the total population previously inputted in the Analysis Properties window (Fig. 5) or the percentage of the total population;8

� the default relative risk values for all-cause mortality: 1.062 (95% CI: 1.040–1.083);9 and

� the default cut-off value recommended by the 2005 WHO AQG: 10 µg/m3; (AirQ+ calculates the burden or impact of exposure when this concentration is exceeded).

Click the Calculate button in the Impact Evaluation window (Fig. 6) to go to the Results window (Fig. 7).

6 AirQ+ uses the total number of adults (≥ 30 years) exposed to the pollutant, a value the user enters when creating a new Impact Analysis, explained later in this section.

7 AAP: ambient air pollution8 Next to the data entry field for population at risk is a button used to change from entering an absolute number (#) to a

percentage (%).9 The relative risk values and the 95% confidence interval (CI) come from the results of the meta-analysis of 13 cohort studies

by Hoek G, Krishnan RM, Beelen R, Peters A, Ostro B, Brunekreef B, et al. (2013). Long-term air pollution exposure and cardio-respiratory mortality: a review. Environ Health. 12(1):43. doi:10.1186/1476-069X-12-43.


Fig. 6. AirQ+ Impact Evaluation window for ambient air pollution – PM2.5 – long-term – adult mortality

Results The Results window indicates that 1112 premature deaths (central estimate; Fig. 7) caused by long-term exposure to PM2.5 could be “avoided” if the concentration of PM2.5 would not exceed 10 µg/m3, the threshold recommended by the 2005 WHO AQG. The values in the Lower and Upper columns correspond to the estimates calculated with, respectively, the lower and upper confidence interval limits of the relative risk. This range is the 95% CI based on the uncertainty in the relative risk values for all-cause mortality that the user entered when creating the Impact Evaluation and shows some of the uncertainty associated with the estimates.

Fig. 7. AirQ+ results for ambient air pollution – PM2.5 – long-term – adult mortality

UncertaintyThe uncertainty associated with the exposure assessment should be assessed by selecting different pollutant concentrations. Other changes in input parameters could be used to study the sensitivity of the impact estimates to various assumptions made.

Users can export the results in csv format by clicking the export icon and selecting the data to export (Fig. 8 and Fig. 9).


Fig. 8. AirQ+ export icon

Fig. 9. AirQ+ export data options


Data input: PM2.5 daily frequency data

This example uses the “CityData_daily_2004-2006.csv” data file.

In the New Impact Assessment window (Fig. 10), select ambient pollution, long-term effects and the pollutant PM2.5 and click OK, as in the previous example.

In the Analysis Properties window (Fig. 11) enter the total population “1690109”. AirQ+ does not use the total population for the analysis, so the user can consider this as additional information. Since the input file contains daily concentration data, select the Input Air Quality Data option. See the subsection “Comments on data input options” for differences between the two input methods.

Fig. 10. AirQ+ New Analysis window for ambient air pollution – PM2.5 – long-term – adult mortality

Fig. 11. AirQ+ Analysis Properties window for ambient air pollution – PM2.5 – long-term – adult mortality (data input: PM2.5 daily frequency data)


After creating a new Impact Evaluation, enter the following data:

� mortality incidence for adults (≥ 30 years), all natural causes, per 100 000 population: 939.73

� the total number of adults (≥ 30 years) exposed to the pollutant: 1156588

� the default relative risk values for all-cause mortality: 1.062 (95% CI 1.040–1.083)

� the default cut-off value recommended by the 2005 WHO AQG: 10 µg/m3.

Click the Enter Air Quality Data button (Fig. 11) to go to the Import Air Quality window (Fig. 12). Choose the appropriate data import options and remember to select PM2.5 data in the Daily Mean drop-down box (highlighted in red in Fig. 12). After importing the data, AirQ+ calculates that the city produced an average PM2.5 concentration of 27.95 µg/m3, and displays this and other results in the Results window (Fig. 13). In this example, the mean value is a three-year average of available daily values.10

Fig. 12. Data import window for ambient air pollution – PM2.5 – long-term – adult mortality (data input: PM2.5 daily mean)

Note: when importing data, it is important to choose the correct Select Input Format radio button. If the input file has a frequency distribution, the Aggregated button needs to be selected so that AirQ+ will aggregate the

10 The user has to check if the availability of data is reasonably representative of the period considered. The mean concentration value must be based on days with valid data. In each year, 75% of days with valid measurements should be available. In the case of CityData, only 15 days lack valid PM data (13 days in 2004 and 2 days in 2006).


daily data. Otherwise AirQ+ interprets the first values in a row as a date, which results in wrong input data. The user can input data manually by using the tab key to add rows to the table with air pollution values.

Fig. 13. AirQ+ Impact Evaluation window for ambient air pollution – PM2.5 – long-term – adult mortality (data input: PM2.5 daily frequency data)

ResultsIn this example, AirQ+ returns the long-term effects using PM2.5 daily frequency data. The Results window indicates that 1113 premature deaths (central estimate; Fig. 14) could be “avoided” if WHO AQG for PM2.5 are respected.

Fig. 14. AirQ+ results for ambient air pollution – PM2.5 – long-term – adult mortality (data input: PM2.5 daily frequency data)

Comments on data input optionsMean values should be computed from original daily (raw) data, not from a frequency distribution. Though obtained mean values can be close, they are not identical and results obtained may differ.



ambient air pollution – PM2.5 – short-term – adult mortality

The user should consider that “[…] quantification of the effects of short-term exposure should be done for information only; it is not proposed as an alternative to quantification of long-term PM2.5 exposure” (WHO, 2013: p. 19).11

Question to be addressed: How many deaths (out of the total annual number of deaths from natural causes) are attributable to short-term (daily) exposure to air pollution exceeding a selected daily concentration (e.g. 25 µg/m3, WHO AQG for daily mean PM2.5)?

In the New Impact Analysis window (Fig. 15), select ambient pollution, short-term effects and the pollutant PM2.5.

Fig. 15. AirQ+ New Impact Assessment window for ambient air pollution – PM2.5 – short-term – adult mortality (data input: PM2.5 daily frequency data)

In the Analysis Properties window (Fig. 16) enter the total population “1690109”. AirQ+ does not use the total population for the analysis, so the user can consider this as additional information. Since the input file contains daily concentration data, select the Input Air Quality Data option; this enables the Enter Air Quality Data button and allows the import of detailed PM2.5 air quality data. See the subsection “Comments on data input solutions” for more information.

11 WHO Regional Office for Europe (2013). Health risks of air pollution in Europe – HRAPIE project. Copenhagen: WHO Regional Office for Europe (http://www.euro.who.int/en/health-topics/environment-and-health/air-quality/publications/2013/health-risks-of-air-pollution-in-europe-hrapie-project.-new-emerging-risks-to-health-from-air-pollution-results-from-the-survey-of-experts, accessed 25 November 2019).


Fig. 16. AirQ+ Analysis Properties window for ambient air pollution – PM2.5 – short-term – adult mortality (data input: PM2.5 daily frequency data)

After the user has created an Impact Evaluation (Fig. 17), enter the following data:

� mortality incidence for adults (≥ 30 years), all natural causes, per 100 000 population: 939.73

� the total number of adults exposed to the pollutant: 1156588;

� the default relative risk values for all-cause mortality: 1.0123 (95% CI 1.0045–1.0201); and

� the default cut-off value recommended by the 2005 WHO AQG: 25 µg/m3.


Fig. 17. AirQ+ Impact Evaluation window for ambient air pollution – PM2.5 – short-term – adult mortality (data input: PM2.5 daily frequency data)

As expected the short-term effects are minor compared to the long-term effects. In fact, the results indicate that 95 excess deaths could be “avoided” if WHO AQG for PM2.5 are respected. It is worth noticing that the effects of short-term exposure to PM2.5 are already included in the estimates of the effects of long-term exposure.



ambient air pollution – PM2.5 – long-term – adult mortality – use of IER

The use of IER functions is suggested when there is interest in cause-specific mortality, exposure to high levels of PM (for example PM2.5 > 40 µg/m3) or in the absence of any regional or local relative risks. The user should always refer to the latest updates of the IER functions.

AirQ+ allows calculations using the most recent IER functions with pre-established cut-off values (WHO AQG and interim targets)12 and using the original IER functions with a fixed cut-off value (see the Glossary). The IER functions are designed for burden of disease calculations (see the AirQ+ burden of disease of air pollution manual) but they can be used for assessing the change in the health impacts of air pollution, if PM2.5 concentration values are inputted for two different estimates as the following example shows. A second example shows the use of the interim targets of the 2005 WHO AQG to see the benefit of the reduction of air pollution. The user is also invited to check the age group to which the IER refers. For example, ischaemic heart disease or stroke are related to adults and the analysis can be performed by age groups 25–29, 30–34, 35–39 … 95+.

Question to be addressed: What is the burden on health from lung cancer in adults at different pollution levels?

This case requires two AirQ+ analyses, one for each pollution level considered. The user should consider that the confidence interval cannot be assumed to equal the difference between the confidence intervals used in the two calculations.

Data input: two PM2.5 yearly average values

This example runs two calculations using two different PM2.5 yearly average values; the first uses 27.95 µg/m3 and the second uses 127.95 µg/m3 (a value chosen by the user). In this example the “GBD 2015/2016 (integrated function 2016)” has been selected in the Calculation Method drop-down menu (figure not shown).13 Once data are inputted in the Analysis Properties and Impact Evaluation windows, the first run of AirQ+ shows that 92 attributable cases and 15.47% of the attributable proportion due to lung cancer are associated with a PM2.5 yearly average of 27.95 µg/m3 (data not shown).

12 The 2005 WHO AQG also provide interim targets for pollutants.13 GBD 2015/2016 is the Global Burden of Disease Study.


After running the second calculation (Fig. 18), use the compare-browser function to compare the two estimates (Fig. 19). Using a higher PM2.5 yearly average value (127.95 µg/m3) leads to a greater impact of 235 premature deaths representing 39.32% of the attributable proportion of adult mortality due to air pollution.

The attributable proportion of adult mortality at the higher pollution level is approximately 23% higher than that at the lower pollution level. Excess deaths (at the higher pollution level) are 61% higher than that at the lower pollution concentration of 27.95 µg/m3 (Fig. 19).

Fig. 18. AirQ+ results for ambient air pollution – PM2.5 – long-term – adult lung cancer mortality – use of IER (mean 127.95 µg/m3)

Fig. 19. AirQ+ results for ambient air pollution – PM2.5 – long-term – adult lung cancer mortality – use of IER at different air pollution levels



ambient air pollution – PM2.5 – long-term – adult mortality – use of interim targets values

Question to be addressed: What health gains are expected in reduced adult lung cancer cases by reducing the current level of air pollution, measured using PM2.5 data, to the interim target-1?

In this case one run of AirQ+ is required.

Data input: PM2.5 yearly average

In the New Impact Assessment window, select ambient pollution, long-term effects and the pollutant PM2.5. In the Analysis Properties window, enter the total adult population: 1156588 and the hypothetical mean value 87.95 µg/m3.

In the Impact Evaluation, enter the following data:

� mortality incidence (lung cancer) per 100 000 population: 51.6

� the total number of adults exposed to the pollutant: 100% (default value).

Relative risk values need not be entered as the IER function is used; for this example the user should select “GBD 2015/2016 (integrated function 2016 vs WHO Interim target-1)” in the Calculation Method drop-down menu.

ResultsThere is a large reduction in lung cancer mortality, due to the high hypothesized incidence, and this produces an estimate that 103 deaths could be “avoided” (Fig. 20).

Fig. 20. AirQ+ results for ambient air pollution – PM2.5 – long-term – adult mortality – use of IER (mean 87.95 µg/m3)



ambient air pollution – ozone – long-term – adult mortality

Question to be addressed: how many deaths (out of the total number of deaths from natural causes) of adults in city 1 are due to long-term exposure to ozone that exceeds the annual mean concentration of 100 µg/m3?

The user can make calculations using two types of air pollution data: (i) SOMO3514 and the number of valid days of measurement (data input: SOMO35); and (ii) daily frequency data for ozone measured as a maximum daily eight-hour mean (for example 10 µg/m3 unit range, data input: ozone daily data).

Data input: SOMO35

In the New Impact Assessment window, select ambient pollution, long-term effects and the pollutant O3. In the Analysis Properties window, enter the total population: 1690109 (Fig. 21). As this example uses daily data, select the Input Air Quality Data option to enable the option of importing detailed SOMO35 air quality data. SOMO35 is calculated by:

Since the eight-hour means are given in µg/m3, change the 35 ppb in the equation to 70 µg/m3. The CityData data SOMO35 is 9.7473 µg/m3.

In fact, SOMO35 uncorrected is 10 653.8 µg/m3 x days and there are 1093 valid days.

In the Impact Evaluation, enter the following data:

� mortality incidence (respiratory diseases) per 100 000 population, hypothetically: 160;

� the total number of adults (≥ 30 years) exposed to the pollutant: 1156588;

� the recommended relative values for respiratory mortality: 1.014 (95%CI 1.005–1.024); and

� the default cut-off value for the eight-hour mean as suggested by the SOMO35 calculations: 70 µg/m3; this corresponds to 35 ppb or a cut off of 0.

With an annual mean ozone concentration of 9.7473 µg/m3, 25 deaths could have been “avoided” if the eight-hour mean was 70 µg/m3 (35 ppb) (Fig. 22).

14 SOMO35: sum of maximum eight-hour ozone levels over 35 ppb is an indicator for health impact assessment recommended by WHO.


Data input: daily ozone data

This is not currently possible in AirQ+. The user must convert the data to SOMO35 using the formula provided in the previous example.

Fig. 21. AirQ+ Analysis Properties window for ambient air pollution – ozone – long-term – adult mortality

Fig. 22. AirQ+ results for ambient air pollution – ozone – long-term – adult mortality

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health impact assessment of air pollution: introductory

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