Emission data (for NOx, VOC, NH 3 , PM10, PM2.5, SO 2 ) and AQI computed
values (mean PM10, mean PM2.5, AOT40, SOMO35, mean NO 2 , mean MAX8H
O 3 ) have been then used to train the Artificial Neural Networks (ANNs), which
describe the relationship between emissions of the precursors and the AQI for each
temporal period (year, winter and summer). The results confirmed that the neural
network surrogate model is capable of reproducing the non-linear relationship
between emissions and precursors.
To train the ANNs, 12 emission scenarios on the Emilia-Romagna domain were
designed and used.
Impact
For the health impact assessment, the high-resolution concentration maps were
combined with a detailed population map. The approach used was retrospective.
The health impact relationship used dealt with the reference values associated to the
relative risks, without thresholds. Population data used for the health impact
functions originated from a cohort study. The air pollutants used in the estimation
were: PM2.5, Arsenic, Cadmium, Nickel and other. The exposure indicators were
calculated based on interpolated monitored data and modeled values. For population, the same spatial and temporal resolution of concentration were used. The
indicator used was the morbidity (e.g. pneumonia cases, cardiovascular and respiratory diseases).
Response
In this preliminary phase of the Regional AQP, the RIAT+ tool has been used to
assess measures and costs to improve air quality. Both technological and efficiency
measure are taken into account in the optimization process. Analyzing the yearly
average PM10 concentration on the whole Emilia-Romagna, a Pareto curve was
obtained, the points of which represents different optimal combinations of reduction
measures. The analysis of the Pareto curve shows that a significant reduction of
NH 3 should be reached acting on agriculture macro sector, while NOx reduction
should be obtained through transport and other mobile sources macro-sectors.
Actions on residential heating should be promoted to reduce a large part of primary
PM10 component.
RIAT+ gave also a detailed list of measures to obtain these reductions. The
combination of different runs with single or multi-pollutant optimization objectives
leads to the following list of priority measures to be implemented:
• Energy efficiency measures in the residential sector including improved
fireplaces;
• High efficiency oil and gas industrial boilers and furnaces in manufacturing
industry;
• Significant replacement of old heavy and light duty diesel vehicles with newer
Euro5 and Euro6 compliant), as well as an increase of the limited traffic zones
and cycling paths;
• Replacement of oldest construction and agriculture vehicles.
The overall plan may thus be represented by the chart in Fig. 3.25.
64
C. Belis et al.
values (mean PM10, mean PM2.5, AOT40, SOMO35, mean NO 2 , mean MAX8H
O 3 ) have been then used to train the Artificial Neural Networks (ANNs), which
describe the relationship between emissions of the precursors and the AQI for each
temporal period (year, winter and summer). The results confirmed that the neural
network surrogate model is capable of reproducing the non-linear relationship
between emissions and precursors.
To train the ANNs, 12 emission scenarios on the Emilia-Romagna domain were
designed and used.
Impact
For the health impact assessment, the high-resolution concentration maps were
combined with a detailed population map. The approach used was retrospective.
The health impact relationship used dealt with the reference values associated to the
relative risks, without thresholds. Population data used for the health impact
functions originated from a cohort study. The air pollutants used in the estimation
were: PM2.5, Arsenic, Cadmium, Nickel and other. The exposure indicators were
calculated based on interpolated monitored data and modeled values. For population, the same spatial and temporal resolution of concentration were used. The
indicator used was the morbidity (e.g. pneumonia cases, cardiovascular and respiratory diseases).
Response
In this preliminary phase of the Regional AQP, the RIAT+ tool has been used to
assess measures and costs to improve air quality. Both technological and efficiency
measure are taken into account in the optimization process. Analyzing the yearly
average PM10 concentration on the whole Emilia-Romagna, a Pareto curve was
obtained, the points of which represents different optimal combinations of reduction
measures. The analysis of the Pareto curve shows that a significant reduction of
NH 3 should be reached acting on agriculture macro sector, while NOx reduction
should be obtained through transport and other mobile sources macro-sectors.
Actions on residential heating should be promoted to reduce a large part of primary
PM10 component.
RIAT+ gave also a detailed list of measures to obtain these reductions. The
combination of different runs with single or multi-pollutant optimization objectives
leads to the following list of priority measures to be implemented:
• Energy efficiency measures in the residential sector including improved
fireplaces;
• High efficiency oil and gas industrial boilers and furnaces in manufacturing
industry;
• Significant replacement of old heavy and light duty diesel vehicles with newer
Euro5 and Euro6 compliant), as well as an increase of the limited traffic zones
and cycling paths;
• Replacement of oldest construction and agriculture vehicles.
The overall plan may thus be represented by the chart in Fig. 3.25.
64
C. Belis et al.