The Environmental Agency of Sao Paulo has a large dataset of carbon monoxide measurements: 20 years of records in 18 automatic stations inside the metropolitan area. However, a thorough investigation on the time evolution of CO concentration tendency and cycles also considering spatial variability is lacking. The investigation consists of a trend line analysis, a periodogram analysis, a correlation between CO concentration and meteorological variables, and spatial distribution of CO concentration. Local and federal policies helped in decreasing CO concentrations and the highest decreasing rate was 0.7% per month. This tendency is lately stabilizing, since the vehicles fleet is increasing. CO most relevant cycles are annual and diurnal and a few series indicate a weekly cycle. Diurnal cycle shows two peaks, morning and evening rush hours, 1.2 and 1.1 ppm, respectively, in 2012. However, lately there is an extended evening peak (20 h to 23 h), related to changes in emission patterns. The spatial analysis showed that CO concentration has high spatial variability and is influenced by proximity to heavy traffic and vegetated areas. The present work indicates that several processes affect CO concentration and these results form a valuable basis for other studies involving air quality modeling, mitigation, and urban planning.
Air pollution caused by particulate matter (PM), which includes nitrates, sulfates, and black carbon, has been increasing in urban areas, particularly in low and middle-income cities [
The Metropolitan Region of Sao Paulo (MRSP) is formed by 39 cities, including Sao Paulo (23°32′56′′S, 46°38′20′′W), the largest city in the South Hemisphere, and has deteriorated air quality that increases the mortality of its 21 million inhabitants [
The major source of CO and HC is light vehicles and, according to the Environmental Agency (CETESB), in 2014 the MRSP vehicular fleet was composed of 6.1 million light-duty vehicles (5.1 million private and 1 million commercial), 181 thousand heavy-duty vehicles, 57 thousand buses, and 896 thousand motorcycles [
The temporal and spatial variation of CO concentration is dependent on meteorological conditions as well as emissions patterns. Meteorological conditions are influenced by phenomena of different scales, as the diurnal evolution of the Planetary Boundary Layer (PBL), sea breeze, and synoptic fronts. Emission patterns are influenced by commercial days and hours, vehicle and fuel sales, and a variety of public policies (aiming on mobility, emission, production of vehicles, availability of fuel types, etc.).
A weekly cycle in CO concentration, an indication of vehicular emission influence, has been observed in many urban areas, including Sao Paulo, when the concentration is minimum during the weekend and then maximum on working days [
The influence of meteorological processes can be noticed by the diurnal and seasonal variability observed in CO concentration time series. Zvyagintev et al. [
The elevated concentrations of atmospheric pollutants and the concerns about greenhouse gases emissions lead to the development of public policies that aimed to reduce these emissions. Therefore, in 1986, the National Environmental Council of Brazil (CONAMA) created the Vehicular Air Pollution Control Program (PROCONVE) that established limits for the vehicular emission and forced the development of new technologies regarding fuels, engines, and car parts [
PROCONVE has proved to help decrease CO concentration in the MRSP, where it has not exceeded the air quality standard limit since 2008 and presents a decreasing tendency. Carvalho et al. [
Since vehicular emission is also linked to mobility problems in urban areas, other public policies have been implemented locally. In the city of Sao Paulo, vehicles are restricted in the center of the city (delimited by the thick black line in Figure
Spatial distribution of the monitoring stations in the MRSP. Each marker represents a station that has recorded CO concentration between 1996 and 2013. Black thick line represents the traffic restriction area for light-duty vehicles. SAC and SAP stations are very close and are represented by the same marker. SAP has substituted SAC station (source: OpenStreetMap, 06 Jul 2016).
On the other hand, the vehicular fleet in the MRSP was estimated to be over 7 million vehicles (5 million light vehicles) in 2013 [
CETESB has been monitoring the air quality since 1970s and has recorded CO measurements in 18 monitoring stations in the MRSP, providing a relevant dataset for analysis of long-term variations in CO concentration patterns and spatial variability. However, to our best knowledge, studies using this dataset have focused on linear trends or cycles representing an average behavior for the MRSP [
This work used the database from CETESB, from 1 May 1996 to 30 Jun 2013. The database comprises 33 automatic monitoring stations inside the MRSP and, from this total, 18 stations have recorded CO concentrations, although some stations were installed after 1996 and others were closed before 2013. SP1, SP3, SP4, SP6, and OS stations (Figure
Monitoring stations inside the Metropolitan Area of Sao Paulo and their main characteristics.
Station code | Location (lat, lon) | Dataset period | Main characteristics |
---|---|---|---|
SP1 | 23°32′40.36′′S | May 1996 to Jun 2013 | Vegetated area, near heavy traffic highways and railways/restriction zone |
46°37′39.34′′W | |||
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SP2 | 23°32′57.87′′S | Jan 2007 to Jul 2012 | Inside a park, near highway/inside restriction zone |
46°33′54.18′′W | |||
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SP3 | 23°35′27.97′′S | Feb 1996 to Jun 2013 | Inside a park/inside restriction zone |
46°39′36.53′′W | |||
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SP4 | 23°36′56.15′′S | May 1996 to Jun 2013 | Near an airport and a highway/beside restriction zone |
46°39′46.68′′W | |||
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SP5 | 23°30′31.17′′S | Aug 1996 to Jan 2003 | Near heavy traffic highways/inside restriction zone |
46°42′4.13′′W | |||
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SP6 | 23°33′10.22′′S | May 1996 to Jun 2013 | Near heavy traffic/inside restriction zone |
46°40′20.04′′W | |||
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SP7 | 23°32′49.11′′S | May 1996 to Feb 2010 | Commercial area, heavy traffic/inside restriction zone |
46°38′31.84′′W | |||
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SP8 | 23°33′38.72′′S | Sep 2001 to Jun 2013 | Near heavy traffic highway/inside restriction zone |
46°42′5.67′′W | |||
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SP9 | 23°46′33.49′′S | Jun 2007 to Jun 2013 | Near highway and vegetated areas |
46°41′49.04′′W | |||
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SP10 | 23°33′57.26′′S | Jan 2007 to Jun 2013 | Inside a university campus/beside restriction zone |
46°44′14.54′′W | |||
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SP11 | 23°31′6.26′′S | Sep 2012 to Jun 2013 | Near heavy traffic highways/inside restriction zone |
46°44′35.95′′W | |||
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SP12 | 23°39′15.29′′S | Jun 1996 to Dec 2010 | Inside a park |
46°42′34.18′′W | |||
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SC | 23°37′5.41′′S | Jun 1996 to Jun 2013 | Commercial area, heavy traffic |
46°33′22.52′′W | |||
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SAC | 23°39′22.64′′S | Feb 1998 to Oct 2007 | Vegetated area, near heavy traffic |
46°31′49.06′′W | |||
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SAP | 23°39′24.18′′S | Jun 2009 to Jun 2013 | Commercial area, heavy traffic |
46°31′51.53′′W | |||
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OS | 23°31′33.70′′S | May 1996 to Jun 2013 | Near heavy traffic highways |
46°47′29.62′′W | |||
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TS | 23°36′30.90′′S | Mar 2005 to Jun 2013 | Near heavy traffic highways |
46°45′28.08′′W | |||
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CA | 23°31′51.91′′S | Feb 2012 to Jun 2013 | Near heavy traffic highways |
46°50′8.48′′W |
Nondispersive infrared sensors are used to measure CO concentrations and an hourly average is recorded with only 1 significant digit after the decimal point. To insure the quality of the measurements, the hourly average is recorded only when at least 75% of the measurements are valid [
There are 12 stations inside Sao Paulo city (SP1 to SP12), distributed among all the 5 zones of the city (north, east, south, west, and center), though more concentrated in the center and less concentrated in the east and south zones, and 6 stations in surrounding cities (SC, SAC, OS, TS, CA, and SAP), as displayed in Figure
The database for each station has hourly averages of pollutants concentrations and meteorological variables obtained simultaneously. However, most of the series does not have the 0500 LT average because the sensors are usually calibrated at this time of the day. The data recorded since 1998 can now be accessed at the Environmental Agency website [
To better understand the trend in CO concentration, taking into account spatial differences, a tendency analysis was performed for each monitoring station. Monthly averages were calculated for each station and time series with these averages were produced. After that, a trend line was fitted and the coefficient of determination
A periodogram analysis was performed to investigate the most important periods of variation in CO concentration, using a python function (scipy.signal.periodogram). The periodogram represents the Fourier Power Spectrum Density of a time series and was used to find periodicity in CO concentration time series. This method uses a Fast Fourier Transform (FFT) technique to calculate the discrete Fourier transform of the time series and estimates the spectrum with the squared magnitude of this transform [
The hourly average series were investigated to find gaps, since only a complete series could be used in the periodogram analysis. When there was only one record missing, for example, at 0500 LT (that was usually missing), it was filled by the average between the previous and the following record. If the previous or the following record were missing and there was at least three records for the same hour in the same month, the gap was filled by the average of the concentrations at the same hour of the same month. If a gap greater than 27 days was found, the series was divided. For example, SP1 station time series began in May 1996, but presented 3 gaps (from 1 Feb 1998 to 28 Feb 1998, from 4 Feb 2004 to 30 Set 2005, and from 1 Aug 2008 to 31 Dec 2008) and then produced 4 different time series to be used in the periodogram analysis. Only series with more than 10,000 records were considered. After that, the periodogram was produced for each of the 29 resulting series and the 7 highest peaks were indicated. The peaks represent the periods with higher power spectrum density that indicate the more significant periodical variations in the time series. To complement this analysis, hourly and monthly averages were calculated for different years, to provide annual and diurnal cycles.
The correlation between CO concentration and the meteorological variables was calculated using the available monthly averages and a hypothesis test was performed with a confidence interval of 95%. Again,
Then, a multivariate analysis was performed for 6 stations that recorded all the 3 variables and CO concentration (SP1, SP3, SP8, SP11, CA, and SC) using monthly averages. A regression equation relating all the 4 variables was found for each station, with a confidence interval of 95%. When a variable presented a
Annual averages of CO concentration were calculated for each station and then a diagram was produced, using a kriging algorithm of the concentrations in respect to their geographical coordinates, to estimate a spatial distribution in an area comprising all the stations.
To analyze CO concentration evolution, the first aspect is the trend. To help the analysis of these results, the stations are divided into 4 groups. The first one (Group 1 in Table
Best fit and exponential fit trend lines and coefficient of determination (
Station | Group | Series length (year) | Best fit |
|
Exponential fit |
|
|
Decreasing (−)/increasing (+) rate (%) |
---|---|---|---|---|---|---|---|---|
SP1 | 1 | 17 | Logarithm | 76 |
|
76 | 0.0 | −0.6 |
SP3 | 1 | 17 | Logarithm | 39 |
|
31 | 0.0 | −0.3 |
SP4 | 1 | 17 | Logarithm | 87 |
|
84 | 0.0 | −0.6 |
SP6 | 1 | 17 | Logarithm | 77 |
|
73 | 0.0 | −0.5 |
SP7 | 1 | 14 | Logarithm | 76 |
|
76 | 0.0 | −0.7 |
SP12 | 1 | 17 | Logarithm | 27 |
|
26 | 0.0 | −0.3 |
SC | 1 | 17 | Logarithm | 44 |
|
44 | 0.0 | −0.3 |
OS | 1 | 17 | Second-degree polynomial | 70 |
|
68 | 0.0 | −0.4 |
SP5 | 2 | 8,5 | Second-degree polynomial | 23 |
|
19 | 0.0 | −0.5 |
SAC | 2 | 11,5 | Second-degree polynomial | 23 |
|
16 | 0.0 | −0.3 |
SP8 | 3 | 11,8 | Exponential | 18 |
|
18 | 0.03 | −0.3 |
TS | 3 | 9 | Exponential | 20 |
|
20 | 0.03 | −0.5 |
SP2 | 4 | 7,8 | Second-degree polynomial | 12 |
|
8 | 0.02 | −0.3 |
SP9 | 4 | 6 | Exponential | 6 |
|
6 | 0.07 | +0.3 |
SP10 | 4 | 6,5 | Third-degree polynomial | 24 |
|
14 | 0.12 | −0.7 |
SAP | 4 | 4 | Second-degree polynomial | 6 |
|
5 | 0.26 | −0.2 |
Group 1 presented larger values for
Scatter plot of monthly averages for stations (a) SP4 and (b) SP9. Red dotted line in the trend line follows the function
Only OS station time series presented a polynomial function as the best fit; however
The series in Group 2, SAC and SP5, were better fitted to a polynomial function and the exponential fit presents a decreasing rate of 0.3 and 0.5% per month, respectively. SP8 and TS (Group 3) time series were fitted to exponential curves, with decreasing rates of 0.3 and 0.5% per month, respectively. However, only a small percentage of the variation of the series may be explained by the curves for groups 2 and 3.
Group 4 presents a tendency of increase of CO concentration by the end of the series; however they have small values for the coefficient of determination and only station SP2 rejects the null hypothesis (with a confidence interval of 95%), meaning that they may have no tendency at all, as illustrated by SP9 series (Figure
To validate the analysis, the same process was performed using yearly averages (not shown here). The results corroborated the values presented in Table
It is noticeable that the trend lines for older/longer series are strongly related to the effect of emission control policies, particularly PROCONVE. The implementation of the program included adequacy of catalysts, improvement of mixture formation, usage of electronic fuel injection, and electronic control module, associated with the renewal of the vehicular fleet, and has successfully mitigated the air pollution caused by CO. Nevertheless, during the series period, another public policy is in effect: the restriction of 20% of the vehicular fleet (by the last digit of the car plate) in the extended central area of the city of Sao Paulo (thick black line in Figure
Since the trend line represents only part of the time evolution of CO concentration, other variations must be investigated. As previously explained, the series of hourly averages of CO concentration for each station were prepared to be used by a periodogram analysis. The periodogram for SP6 station (Figure
Periodogram for SP6 station hourly average time series. The 7 peaks with highest spectral density represent the periods: 17.2 years, 12 hours, 1 year, 8.6 years, 5.7 years, 24 hours, and 7 days. The frequency unit is cycles per hour (CPH) and PSD is the power spectrum density.
The 7 highest peaks for each series were analyzed and showed that the 24-hour, 12-hour, and 1-year periods are the most frequent of all values. As expected, the 1-year period is not present at shorter series. Therefore, the CO concentration evolution has strong semidiurnal, diurnal, and seasonal cycles. Some of the longest series present a period equal to their length, suggesting a strong tendency in time, related to the decreasing trend already analyzed. Other periods that appeared more than once in the analysis are 6 months (seasonal behavior) and 7 days (weekly traffic pattern).
The diurnal and annual cycles of CO concentration were then calculated by averaging CO concentrations for each year (considering all the stations) by hour (diurnal) and month (annual). Figure
(a) Hourly and (b) monthly averages of CO concentration for the MRSP for the years 1997 (blue) and 2012 (black). 10 stations were considered for 1997 and 14 stations for 2012. Error bars represent the standard deviation.
The annual pattern (Figure
To evaluate the impact of meteorological conditions on the time evolution of CO concentration, the influence of the meteorological variables was analyzed by calculating the correlation between the CO concentration and each of the following variables: air temperature, wind speed, and relative humidity. The correlation was calculated using monthly averages of CO concentration and of each meteorological variable. The hypothesis test was also calculated (Table
Correlation coefficient (
Station | Relative humidity | Temperature | Wind speed | |||
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|
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|
SP1 | −0.267 | 0.01 | −0.28 | 0.01 | −0.187 | 0.05 |
SP2 | — | — | — | — | 0.213 | 0.15 |
SP3 | −0.108 | 0.20 | −0.601 | 0.0 | −0.375 | 0.0 |
SP5 | — | — | — | — | −0.325 | 0.01 |
SP8 | −0.098 | 0.40 | −0.527 | 0.0 | −0.376 | 0.0 |
SP9 | −0.438 | 0.09 | −0.116 | 0.55 | — | — |
SP11 | −0.198 | 0.56 | −0.139 | 0.68 | −0.772 | 0.01 |
SP12 | — | — | — | — | −0.154 | 0.04 |
OS | — | — | — | — | −0.082 | 0.25 |
SC | −0.36 | 0.0 | −0.352 | 0.0 | −0.071 | 0.39 |
CA | 0.011 | 0.97 | −0.659 | 0.01 | −0.695 | 0.0 |
SAC | — | — | — | — | −0.178 | 0.09 |
When correlating the CO concentration to the air temperature, 5 out of 7 stations rejected the null hypothesis, three of them presenting moderate inverse correlation (CA, SP3, and SP8), and two presenting weak inverse correlations (SP1 and SC), indicating that CO concentration decreases when the temperature increases. This pattern is again related to the seasonal cycle of CO concentrations (higher during winter and lower during summer). However, it may also be related to the PBL development, since colder months tend to have lower PBL heights [
For the wind speed, 11 stations were analyzed and only 4 fail to reject the null hypothesis. Most stations presented weak inverse correlation, except SP11 and CA that presented moderate to strong inverse correlation. These stations also presented a relatively higher averaged wind speed (not shown here) than the others (approximately 2 ms−1), indicating that the wind is more correlated to CO when its velocity is higher. High wind speeds enhance the turbulence and increase the dispersion and transport of CO, decreasing its concentration. When the wind speed is low, the CO emitted tends to concentrate near its sources.
A multivariate analysis was then performed to investigate the relative effect of temperature, relative humidity, and wind velocity on CO concentration. Table
Regression equation for CO concentration (CO), temperature (
Station | Regression equation |
|
|
Alternative regression equation |
|
|
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SP1 | CO = 4.703 − 0.0347 |
20.0 | 0.0 | CO = 4.547 − 0.0376 |
17.8 | 0.0 |
SP3 | CO = 3.029 − 0.0703 |
41.1 | 0.0 | CO = 2.626 − 0.0724 |
40.0 | 0.0 |
SP8 | CO = 5.765 − 0.0746 |
49.9 | 0.0 | |||
SP11 | CO = 2.779 + 0.0265 |
87.3 | 0.002 | |||
CA | CO = 2.705 − 0.0258 |
81.9 | 0.0 | |||
SC | CO = 5.297 − 0.0663 |
35.7 | 0.0 | CO = 5.163 − 0.0676 |
35.5 | 0.0 |
Since there are significant differences among the stations regarding temporal evolutions and correlation to meteorological variables, a spatial analysis was performed using annual averages. Figure
Spatial distribution of the annual average of CO concentration during (a) 1997, (b) 2008, and (c) 2012. CO concentration is in ppm. Contour interval is 0.2 ppm. Obs.: Color scale is different between (a) and (b, c).
Annual averages do not follow a spatial pattern. For example, stations SP3 and SP9 are farther apart than SP3 and SP4 and yet the former stations have similar averages, while SP4 has the highest average among them. These differences seem to be related to traffic intensity and proximity, since the correlation between each meteorological variable and CO concentration is mostly weak or nonexistent and greater scale meteorological phenomena, such as cold fronts, sea breeze, and synoptic events, are expected to affect the whole MRSP similarly. Some stations present the highest values throughout the years (SP4 and OS) while some others sustain the lowest values (SP12, SP3, SP2, and SP10). A visual analysis of the stations surrounding areas using satellite images indicates that the stations with high annual averages are located near heavy traffic highways and scarcely vegetated areas (Figure
Image of stations. (a) SP4, the airport is at the lower right corner (source: Google Earth, 14 Dec 2008, 08 Dec 2014) and (b) OS (source: Google Earth, 22 Sep 2014, 08 Dec 2014).
Image of stations. (a) SP10 (source: Google Earth, 16 Oct 2014, 08 Dec 2014), (b) SP3 (source: Google Earth, 14 Dec 2008, 08 Dec 2014), (c) SP2 (source: Google Earth, 16 Oct 2014, 08 Dec 2014), and (d) SP12 (source: Google Earth, 14 Dec 2008, 08 Dec 2014).
Image of station SP9 (source: Google Earth, 04 Jul 2014, 08 Dec 2014).
Figure
The observed spatial distribution of the CO concentration and the complexity of the urban mosaic found at the MRSP suggest that there are spatial details that are missed by the present monitoring network, particularly considering the scarcity of stations in the east and south zones of the city. Hence, the use of a numerical model may be the solution to further investigate the patterns of spatial distribution and temporal evolution of CO concentration at the MRSP. The present analysis shows that the local characteristics of each station must be considered when comparing results from numerical models to the observations, or when using the recorded data for decision-making processes. It also indicates that a careful representation of emission patterns, land use types, and spatial distribution of sources are key in studying and modeling air quality in Sao Paulo.
In this work, 17 years of measurements taken by 18 air quality monitoring stations in the MRSP were analyzed in respect of temporal evolution and spatial distribution, aiming to present a thorough investigation about the temporal trends and cycles of CO concentration and their relationship to meteorological conditions and to local factors, to provide a better understanding of CO concentration patterns for modeling and policy studies.
Although it is difficult to link the CO concentration tendency to the public policies, the temporal evolution and the periodogram analysis pointed out that the federal control program (PROCONVE) was able to decrease the concentration of this pollutant at rates from 0.7 to 0.3% per month from 1996 to 2013. This program was probably aided by a local policy in the city of Sao Paulo that prevented the traffic of 20% of the vehicles in the central area of the city, during rush hours in weekdays. The vehicular inspection, implemented in 2009, may have also helped the decreasing tendency of later years, although it is not possible to determine its exact effect by the present analysis. Recently, the concentrations presented a stabilizing (or even localized increasing) tendency, probably caused by the increasing amount of vehicles, suggesting that other forms of public policies must be implemented, following recent attempts, that aim to reduce private vehicular transport and increase the use of public transportation, as well as establishing smaller emission factors for pollutants.
The periodogram analysis shows that the most important periods of variation of CO concentration are 1 day, 12 hours, and 1 year, suggesting that CO follows a diurnal and an annual cycle. Both cycles present an influence of the decreasing tendency mentioned above; however the diurnal cycle has evolved to a different pattern. The evening rush hour seems to be extended, indicating a change in driving behavior, caused by the restriction period (from 1700 LT to 2000 LT), particularly for stations located inside or near the restriction zone. For modeling purposes, a weekly cycle may also be considered. These results, combined with the investigation of the correlation of the CO to humidity, temperature, and wind speed, suggest that CO concentration is greatly influenced by traffic emissions and PBL development, because when the vertical mixing is expected to be higher (during the afternoon and during spring/summer months), CO concentrations are lower, and when the PBL is less developed (during mornings and after sunset, particularly during winter), the CO concentrations are higher. Wind speed was the variable that presented higher correlation to CO concentration, particularly for stations with higher averaged wind speed.
The spatial distribution reinforces the dependence of CO concentrations on traffic emissions and local effects, like presence of vegetation, proximity to heavy traffic, facilities, and so forth. Some areas, such as SP9 station, indicate the necessity of mitigation policies. Depending on the causes for the increase in CO concentration, the actions may range from improving public transport infrastructure to developing educational programs that encourage the use of public transportation. This result indicates that a numerical study, through an appropriately detailed urban model, could enhance the understanding of CO spatial distribution in the MRSP. It also suggests that the characteristics of each station need to be considered when analyzing these data, either for model validation or for decision-making purposes.
The present work has provided a complete analysis of the temporal and spatial evolution of CO in Sao Paulo that can be used as verification for modeling studies and as basis for policy efficiency studies and decision-making processes. The analysis provides evidence of changes in CO concentration tendency and diurnal cycle and considerable spatial variability within the metropolitan area. However, more investigation is needed to establish the relative contribution of local characteristics, meteorological conditions, local and federal public policies, and changes in driving behavior in determining the evolution of CO temporal evolution and spatial distribution in the MRSP.
See Figures
The authors declare that there are no competing interests regarding the publication of this paper.
The authors would like to thank the Fundação de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP), Grant no. 2014/04372-2, the Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq), Grant no. 443029/2014-8, and the University of Sao Paulo for funding this work and the Environmental Agency of Sao Paulo (CETESB) for providing the data.