This document discusses a land use regression (LUR) model created to predict levels of black carbon (BC) pollution along school routes in Milan, Italy. The model was based on data from two seasonal monitoring campaigns measuring BC levels at various sites. Personal exposure to BC was also assessed for 42 schoolchildren using portable monitors and GPS tracking. The LUR model showed moderate correlation (r=0.74) in predicting personal BC exposure levels compared to monitored data, indicating it may be capable of identifying cleaner routes to school. Further refinement of the model and analysis of additional schools is suggested.

1. Is a Land Use Regression model
capable of predicting the cleanest
route to school?
Dr. Luca Boniardi, Prof.ssa Silvia Fustinoni
luca.boniardi@unimi.it
30/01/2020, VII giornata sulla modellistica in ARIA(NET)

2. The MAPS MI project
“Mapping Air Pollution in a School catchment area of Milan with a participatory approach”
Two seasonal monitoring campaigns to study and model
spatial and temporal variability of Black Carbon (BC)
Ludical and experience-based laboratories to involve and
engage third-grade schoolchildren
Two seasonal personal exposure assessment campaigns,
with air pollutants, biological and microbiota monitoring
methods and time-activity survey
2017-2018
2018
2018-2019
1/15

3. Why Black Carbon?
2/15
 because of its small size it enters the airways in depth (WHO, 2012)
 sufficient evidence of associations of all-cause and cardiopulmonary mortality
with long-term average BC exposure (WHO, 2012)
 sufficient evidence of associations of short-term (daily) variations in BC
concentrations with short-term changes in health (all-cause and cardiovascular
mortality, and cardiopulmonary hospital admissions) (WHO, 2012)
 BC exposure is linked with inflammation markers in children (De Prins, 2014)
 Studies of short-term health effects show that the associations with BC are more
robust than those with PM2.5 or PM10 (Janssen NAH, 2011)
 It is the second main concern after CO2 if considering climate change (EPA, 2012)
 Black Carbon (BC) can be defined as the most strongly light-
absorbing component of particulate matter (PM) (EPA, 2012)
 it is a primary pollutant formed by the incomplete combustion
of biofuel, biomass and fossil fuel (EPA, 2012)
 it is an additional marker to PM2.5 to evaluate the
cost/effectiveness of pollution control local policy (UNECE-
CLRTAP, 2013)
 once emitted it behaves like gases: during the traffic rush hour
the PM10/BC ratio decreases (Reche C. 2011)
 “as a tracer of exposure to traffic, it offers the possibility to
check the effectiveness of mobility policies at the scale 'local'
with regard to the health effects” (Invernizzi et al. 2013)

4. Step I:
Spatial analysis and modelization
of Black Carbon diffusion
3/15
May-June 2017/January-February 2018

5. The study area
Study area:≈ 25 km2
Involved parents = 16 (45%)
Urban Background sites = 7 (20%)
Urban Traffic sites = 21 (62%)
Street sites = 6 (18%)
+1 reference site (at school)
4/15

6. N. 1
MA200
N. 8
AE51
Monitoring sites and optical devices
5/15

7. Boxplots and hourly trends
Spatial contrast among street (S), urban background (UB)
and urban traffic (UT) sites, during MRH
6/15
Tests of significance (Kruskal-Wallis and post hoc):
• UB and S sites are always significantly different
(p<.01)
• S and UT sites are significantly different only
during MRH (p<.01)
• UB and UT sites are never significantly different

8. 7/15
Land Use Regression (LUR) technique
How to collect possible predictors

9. Model
Intercept
(ng/m3)
Variable 1 Variable 2 R2 LOOcV
R2
RMSE
(ng/m3)
LOOcV
RMSE
Cold season 3095 TRAFLOAD_50b - .52 .35 304 355
Cold season MRH 3760 TOT_INVDist_MRHa TRAFLOAD_100_MRHb .65 .51 434 509
a TOT_INVDist_MRH: total of the vehicles at X hour/distance to nearest road
b TRAFLOAD_Y_X: sum of the products of the total vehicles per length of the
roads in a circular buffer of radius Y at hour X
෍
𝐾=𝑚
𝑛
𝐿𝑒𝑛𝑔𝑡ℎ 𝑛 × 𝑇𝑟𝑎𝑓𝑓𝑖𝑐 𝑛 + ⋯ + (𝐿𝑒𝑛𝑔𝑡ℎ 𝑚 × 𝑇𝑟𝑎𝑓𝑓𝑖𝑐 𝑚)
Land Use Regression (LUR) models
Variables and parameters
8/15

10. Step II:
Personal exposure assessment
to air pollution
9/15
May-June 2018/January-February 2019

11. Personal monitoring campaign
Personal exposure to air pollutants, GPS tracking
GPS device
Pollutants sampler:
- NO2
- Black Carbon
- BTEX
GPS monitoring
24 hrs 42 schoolchildren
Personal air pollutants
exposure
10/15
Time-Activity
Diaries (TAD)

12. Personal exposure to BC
LUR model application procedure
11/15

13. Is a LUR model capable of predicting
cleanest routes to school?
Correlation plot (A) with confidence interval set at 95% (shadowed area), and reference line (black dashed line). Bland-Altman
plot (B) with average line (blue line), and ±1.96 SD lines (red lines). According to A, Pearson’s correlation coefficient is 0.74.
In the comparison between the two methods, Lin’s Concordance Correlation Coefficient is 0.6 (95% CI: 0.43 - 0.74).
12/15

14. Home-to-school routes map
13/15

15. 14/15

16. To broaden the analysis to other daily time-window
To refine models by incorporating more sophisticated techniques
To integrate temporal variability in the models in order to avoid raw rescaling methods
To find the way to broaden systematically the analysis to all the school of the city
Next Steps
Thank you!
luca.boniardi@unimi.it 15/15