Particles size and composition in Mediterranean countries:
geographical variability and short-term health effects
MED-PARTICLES Project 2011-2013
Under the Grant Agreement EU LIFE+ ENV/IT/327
Particles size and composition in Mediterranean countries:
geographical variability and short-term health effects
MED-PARTICLES
ACTION 3.
Data collection on: daily concentrations of particles (PM10, PM2.5-10; PM2.5), gases (CO,
NO2, O3, SO2) and meteorological parameters (air temperature, humidity, sea-level
barometric pressure, wind speed/direction)
Summary: protocol to collect in a systematic and comprehensive way data on environmental variables
(daily concentrations of particles, daily concentrations of gases, daily averages of meteorological
parameters) in the Mediterranean cities involved in the project. In addition, other daily variables on
potential relevant confounders will be collected.
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Objective
To collect, for each participating city and for each day of the study period, the relevant environmental
data on daily particles concentrations, gases and meteorological parameters to be put in relationship
with health endpoints (Action 6). In addition, even though the relevant exposure variables will be daily
summaries, it is proposed to also provide hourly values for descriptive purposes, especially concerning
the possible origins of different pollutants according to time of the day. The particles sizes of interest
within the Med-Particles project are PM10 (particulate matter with aerodynamic diameter < 10
microns), PM2.5 (PM with diameter < 2.5 microns) and PM2.5-10 (PM with diameter between 2.5 and
10 microns). Data will be collected on the following gases: CO, NO2, O3, SO2, and on the following
meteorological parameters: air temperature, relative humidity/dew point temperature, sea-level
barometric pressure, wind speed/direction). In addition, other daily variables on potentially relevant
confounders will be collected. They include: day of the week, holiday, summer population decrease,
influenza epidemics.
The data collection will be performed at the center-level and finally the city-specific data will be pooled
centrally (Action 7).
Methods
Hourly and daily concentrations of particles (PM10, PM2.5-10, PM2.5)
For each city involved in the project, and for each day of the study period, data on daily concentrations
of particles will be collected from the existing monitoring networks. Data on three different PM sizes
will be collected: PM10, PM2.5 (fine fraction), PM2.5-10 (coarse fraction). In particular, for each PM
size, data from three different types of monitors will be collected: one close to traffic sources, one
located in an urban background context, and one located in a rural setting. Whereas possible, data from
more than one monitor for each type will be provided. Since PM2.5-10 might not be directly available,
it can be computed as the difference between PM10 and PM2.5 as long as the same monitoring stations
are used for PM10 and PM2.5. Therefore, in case of multiple monitors available, it is suggested to use
those for which data on both PM10 and PM2.5 are collected.
If data are available at an hourly detail, they will be collected together with daily averages, which will be
computed from the hourly measurements. For the calculation of the daily average from the hourly
measurements, at least 13 hourly values must be available, with no more than 6 consecutive missing
values
Once data from different monitors are retrieved for each PM size, they will be pooled together to get
an estimate of the daily averages for each size and each city, and specific procedures for the imputation
of missing values will be defined. This will be accomplished in a separate Action (Action 7).
Hourly and daily concentrations of gases (CO, NO2, O3, SO2)
For each city involved in the project, and for each hour of each day of the study period, data on hourly
concentrations of 4 different gases, CO, NO2, O3, SO2, will be collected from the existing monitoring
networks. In particular, for each gas, data from three different types of monitors will be collected: one
close to traffic sources, one located in an urban background context, and one located in a rural setting.
Whereas possible, data from more than one monitor for each type will be provided. Once the hourly
values are collected, they will be averaged to get daily concentrations. The following criteria will be
applied:

NO2 and SO2: the daily average will be computed. For the calculation of the daily average from
the hourly measurements, at least 13 hourly values must be available, with no more than 6
consecutive missing values;

O3 and CO: the daily maximum 8-hour running mean will be computed from the hourly
measurements. At least 18 8-hour running means must be available, each one of them
calculated with at least 6 hourly values. The 8-hour running means for each day are 24, starting
from the time window 5pm (of the day before)-1am, to the time window 4pm-12pm.
Once data from different monitors are retrieved for each gas, they will be pooled together to get an
estimate of the daily averages for each city, and specific procedures for the imputation of missing values
will be defined. This will be accomplished in a separate Action (Action 7).
Three-hour measurements and daily summaries on meteorological parameters (air temperature, relative
humidity/dew point temperature, apparent temperature, sea-level barometric pressure, wind
speed/direction)
For each city involved in the project, and for each 3-hour period of each day of the study period, data
on meteorological parameters: air temperature, relative humidity/dew point temperature, apparent
temperature, sea-level barometric pressure, wind speed/direction, will be collected. Data will be
collected from the closest airport monitoring station, where available, or from another monitor located
inside the city, if airport data are not available. Apparent temperature is a composite index computed
from air temperature and dew point temperature using the following formula:
Tapp = -2.653 + 0.994*Temp + 0.0153*Dew2
It has to be applied on each 3-hour value of air temperature and dew point temperature.
Once the three-hour values are collected (or computed for apparent temperature), they will be averaged
to get daily summaries.
The following daily summaries will be computed:

Air temperature (°C): daily average, daily minimum, daily maximum

Apparent temperature (°C): daily average, daily minimum, daily maximum

Dew point temperature (°C): daily average

Relative humidity (%): daily average

Sea-level barometric pressure (hPa): daily average

Wind speed (m/second): daily average

Wind direction (degrees): daily average
To compute the daily average for one specific day, at least 5 three-hour values must be available for
that day, otherwise it is set to missing. To compute the daily minimum value, at least one of the 3-hour
values at 0.00, 3.00 or 6.00 must be available. Similarly, to compute the daily maximum value, at least
one of the 3-hour values at 12.00, 15.00 or 18.00 must be available.
NOTE: if data are available for every hour (instead for each 3-hour time window), the same criteria as
those specified for air pollutants will be applied for the computation of the daily averages. For the
computation of daily minimum (maximum) values, at least one of the 6 hourly values between 0.00 and
6.00 (12.00 and 18.00) must be available.
Daily variables on potentially relevant confounders (day of the week, holiday, summer population
decrease, influenza epidemics)
In addition to meteorological parameters (see above), other important variables will be collected for
each city involved in the project, and for each day of the study period, aiming at better adjusting for
daily confounders in the analysis of short-term effects of air pollutants. These variables are detailed
below:

Day of the week: it is a categorical variable, easily obtained from year, month and day (variables
yy mm dd of the code book). It assumes value 0 for Sunday, 1 for Monday, …., 6 for Saturday;

Holiday: it is a categorical variable aiming at taking into account holiday periods or single days
in which most of the population is likely to be outside the study area, and is not at risk of
experimenting the study outcomes. This is very important when studying the effects of
environmental exposures on mortality, but it is fundamental when studying the effects on
hospitalizations. Therefore, in the Italian EPIAIR project, we built two different variables for
the two cases:
o Effect on mortality, variable hday1: it is a categorical 2-level variable, assuming value 0
for normal days, 1 for holidays. All national and city-specific holidays are included, but
not Sundays, which are already accounted for by the “day of the week” variable.
Therefore, if a holiday falls on a Sunday, this variable should assume value “zero” to
avoid taking into account of that day twice.
o Effect on hospitalizations, variable hday2: it is a modified version of hday1, therefore
hday1 must be created first, and then modified as follows. Variable hday2 assumes value
“3” on Jan 1, Easter and the Monday right after Easter, Dec 25 and 26; variable hday2
assumes value “2” on Jan 2, the two days before Easter, Dec 24, and from Dec 27 to
Dec 31.

Summer population decrease: it is a categorical variable aiming at taking into account summer
periods in which most of the population is outside the study area, and is not at risk of
experimenting the study outcomes. There is not a standard way to build this variable, and it is
very country-specific. In the Italian EPIAIR project, it is built equally for mortality and
hospitalizations, as follows:
o Variable popday: 3-level variable assuming value “2” in the 2-week period around the 15th
of August (from Saturday to Sunday, 16 days/year in total); value “1” from the 16 th of
July to 31st of August (with the exception of the 16 days aforementioned); value “0” all
other days.

Influenza epidemics: it is a categorical 2-level variable assuming value “0” on normal days, and
value “1” on days with particularly high influenza episodes. Even in this case, it is not
straightforward to define what “days with particularly high influenza episodes” means. In the
Italian EPIAIR project, we have used the Italian influenza surveillance system from the NIH,
and we defined, for each year, the influenza epidemic period as the three-week period
(consecutive weeks or not) with highest flu incidence in each city investigated. If a similar
system is available in other countries, it is suggested to be used in a similar way. Otherwise, the
weekly flu incidence can be estimated from the hospitalization series, and the influenza
epidemics indicator can be calculated accordingly.
In summary, two different dataset will be produced: one with daily summaries (named
“city_environ_daily_YYbeg_YYend”),
and
another
with
hourly
values
(named
“city_environ_hourly_YYbeg_YYend”) with corresponding code books as detailed below (replace “city”
with the name of the city, “YYbeg” with the year of beginning of the time-series, and “YYend” with
the year of end of the time-series). The first dataset will be used in the epidemiological analysis, while
the second will be used, if needed, for descriptive aims or quality control.
Daily dataset:
Variable
Format
Description
city
Character
Name of the city
yy
Numeric
Year of death
mm
Numeric
Month of death
dd
Numeric
Day of death
dow
Categorical
Day of week, as specified above
hday1
Categorical
Holiday, for effects on mortality, as specified above
hday2
Categorical
Holiday, for effects on hospitalizations, as specified above
popday
Categorical
Summer population decrease, as specified above
fluepi
Categorical
Influenza epidemic, as specified above
pm10_rural1
Numeric
Daily mean concentration of PM10, rural monitor #1
pm10_rural2
Numeric
Daily mean concentration of PM10, rural monitor #2
pm10_back1
Numeric
Daily mean concentration of PM10, urban background monitor #1
pm10_back2
Numeric
Daily mean concentration of PM10, urban background monitor #2
pm10_traffic1
Numeric
Daily mean concentration of PM10, traffic monitor #1
pm10_traffic2
Numeric
Daily mean concentration of PM10, traffic monitor #2
Numeric
Daily mean concentration of PM25, rural monitor #1
…
…
…
pm25_rural1
pm25_rural2
Numeric
Daily mean concentration of PM25, rural monitor #2
pm25_back1
Numeric
Daily mean concentration of PM25, urban background monitor #1
pm25_back2
Numeric
Daily mean concentration of PM25, urban background monitor #2
pm25_traffic1
Numeric
Daily mean concentration of PM25, traffic monitor #1
pm25_traffic2
Numeric
Daily mean concentration of PM25, traffic monitor #2
…
…
…
pm2510_rural1 Numeric
Daily mean concentration of PM25-10, rural monitor #1
pm2510_rural2 Numeric
Daily mean concentration of PM25-10, rural monitor #2
…
pm2510_back1 Numeric
Daily mean concentration of PM25-10, urban background monitor #1
pm2510_back2 Numeric
Daily mean concentration of PM25-10, urban background monitor #2
…
pm2510_traffic1 Numeric
Daily mean concentration of PM25-10, traffic monitor #1
pm2510_traffic2 Numeric
Daily mean concentration of PM25-10, traffic monitor #2
…
co8h_rural1
Numeric
Daily max 8-hour running mean of CO, rural monitor #1
co8h_rural2
Numeric
Daily max 8-hour running mean of CO, rural monitor #2
co8h_back1
Numeric
Daily max 8-hour running mean of CO, urban background monitor #1
co8h_back2
Numeric
Daily max 8-hour running mean of CO, urban background monitor #2
co8h_traffic1
Numeric
Daily max 8-hour running mean of CO, traffic monitor #1
co8h_traffic2
Numeric
Daily max 8-hour running mean of CO, traffic monitor #2
no2_rural1
Numeric
Daily mean concentration of NO2, rural monitor #1
no2_rural2
Numeric
Daily mean concentration of NO2, rural monitor #2
no2_back1
Numeric
Daily mean concentration of NO2, urban background monitor #1
no2_back2
Numeric
Daily mean concentration of NO2, urban background monitor #2
no2_traffic1
Numeric
Daily mean concentration of NO2, traffic monitor #1
no2_traffic2
Numeric
Daily mean concentration of NO2, traffic monitor #2
o38h_rural1
Numeric
Daily max 8-hour running mean of O3, rural monitor #1
o38h_rural2
Numeric
Daily max 8-hour running mean of O3, rural monitor #2
o38h_back1
Numeric
Daily max 8-hour running mean of O3, urban background monitor #1
o38h_back2
Numeric
Daily max 8-hour running mean of O3, urban background monitor #2
o38h_traffic1
Numeric
Daily max 8-hour running mean of O3, traffic monitor #1
o38h_traffic2
Numeric
Daily max 8-hour running mean of O3, traffic monitor #2
so2_rural1
Numeric
Daily mean concentration of SO2, rural monitor #1
so2_rural2
Numeric
Daily mean concentration of SO2, rural monitor #2
so2_back1
Numeric
Daily mean concentration of SO2, urban background monitor #1
so2_back2
Numeric
Daily mean concentration of SO2, urban background monitor #2
so2_traffic1
Numeric
Daily mean concentration of SO2, traffic monitor #1
so2_traffic2
Numeric
Daily mean concentration of SO2, traffic monitor #2
…
…
…
…
…
…
…
…
…
…
…
…
tempmean
Numeric
Daily mean air temperature
tempmin
Numeric
Daily minimum air temperature
tempmax
Numeric
Daily maximum air temperature
tappmean
Numeric
Daily mean apparent temperature
tappmin
Numeric
Daily minimum apparent temperature
tappmax
Numeric
Daily maximum apparent temperature
dewmean
Numeric
Daily mean dew point temperature
rhmean
Numeric
Daily mean relative humidity
pressmean
Numeric
Daily mean sea-level barometric pressure
windspmean
Numeric
Daily mean wind speed
winddirmean
Numeric
Daily mean wind direction
Hourly dataset (where available):
Variable
Format
Description
city
Character
Name of the city
yy
Numeric
Year
mm
Numeric
Month
dd
Numeric
Day
hh
Numeric
Hour
pm10_rural1_h0
Numeric
Concentration of PM10, rural monitor #1, hour 0
pm10_rural1_h1
Numeric
Concentration of PM10, rural monitor #1, hour 1
pm10_rural1_h23
Numeric
Concentration of PM10, rural monitor #1, hour 23
pm10_rural2_h0
Numeric
Concentration of PM10, rural monitor #2, hour 0
pm10_rural2_h1
Numeric
Concentration of PM10, rural monitor #2, hour 1
Numeric
Concentration of PM10, rural monitor #2, hour 23
pm10_back1_h0
Numeric
Concentration of PM10, urban background monitor #1, hour 0
pm10_back1_h1
Numeric
Concentration of PM10, urban background monitor #1, hour 1
pm10_back1_h23
Numeric
Concentration of PM10, urban background monitor #1, hour 23
pm10_back2_h0
Numeric
Concentration of PM10, urban background monitor #2, hour 0
pm10_back2_h1
Numeric
Concentration of PM10, urban background monitor #2, hour 1
Numeric
Concentration of PM10, urban background monitor #2, hour 23
Numeric
Concentration of PM10, traffic monitor #1, hour 0
…
…
pm10_rural2_h23
…
…
…
pm10_back2_h23
…
pm10_traffic1_h0
pm10_traffic1_h1
Numeric
Concentration of PM10, traffic monitor #1, hour 1
pm10_traffic1_h23
Numeric
Concentration of PM10, traffic monitor #1, hour 23
pm10_traffic2_h0
Numeric
Concentration of PM10, traffic monitor #2, hour 0
pm10_traffic2_h1
Numeric
Concentration of PM10, traffic monitor #2, hour 1
Numeric
Concentration of PM10, traffic monitor #2, hour 23
…
…
pm10_traffic2_h23
…
In addition, the same variables for:

PM2.5: replace “pm10” with “pm25”

PM2.5-10: replace “pm10” with “pm2510”

CO: replace “pm10” with “co”

NO2: replace “pm10” with “no2”

O3: replace “pm10” with “o3”

SO2: replace “pm10” with “so2”
Conclusions: The particles sizes of interest within the Med-Particles project are PM10 (particulate
matter with aerodynamic diameter < 10 microns), PM2.5 (PM with diameter < 2.5 microns) and
PM2.5-10 (PM with diameter between 2.5 and 10 microns). Data are collected on the following gases:
CO, NO2, O3, SO2, and on the following meteorological parameters: air temperature, relative
humidity/dew point temperature, sea-level barometric pressure, wind speed/direction). In addition,
other daily variables on potentially relevant confounders are collected. They include: day of the week,
holiday, summer population decrease, influenza epidemics. Data are collected at the center level and
then pooled together in action 7.