A Representative Survey of PM2.5 Mass

7/31/2019 A Representative Survey of PM2.5 Mass

1/15

Urban air pollution: a representative survey of PM 2.5 massconcentrations in six Brazilian cities

Regina Maura de Miranda &

Maria de Fatima Andrade & Adalgiza Fornaro &

Rosana Astolfo & Paulo Afonso de Andre & Paulo Saldiva

Received: 17 June 2010 /Accepted: 8 December 2010 /Published online: 4 January 2011# The Author(s) 2010. This article is published with open access at Springerlink.com

Abstract In urban areas of Brazil, vehicle emissions arethe principal source of fine particulate matter (PM 2.5 ). TheWorld Health Organization air quality guidelines state that the annual mean concentration of PM 2.5 should be below10 g m

3 . In a collaboration of Brazilian institutions,coordinated by the University of So Paulo School of Medicine and conducted from June 2007 to August 2008,PM 2.5 mass was monitored at sites with high trafficvolumes in six Brazilian state capitals. We employedgravimetry to determine PM 2.5 mass concentrations, reflec-tance to quantify black carbon concentrations, X-rayfluorescence to characterize elemental composition, and ionchromatography to determine the composition and concen-trations of anions and cations. Mean PM 2.5 concentrationsand proportions of black carbon (BC) in the cities of SoPaulo, Rio de Janeiro, Belo Horizonte, Curitiba, Recife, andPorto Alegre were 28.113.6 g m

3 (38% BC), 17.211.2 g m

3 (20% BC), 14.77.7 g m3 (31% BC), 14.4

9.5 g m3 (30% BC), 7.33.1 g m

3 (26% BC), and13.49.9 g m

3 (26% BC), respectively. Sulfur andminerals (Al, Si, Ca, and Fe), derived from fuel combus-

tion and soil resuspension, respectively, were the principalelements of the PM 2.5 mass. We discuss the long-termhealth effects for each metropolitan region in terms of excess mortality risk, which translates to greater healthcare expenditures. This information could prove useful todecision makers at local environmental agencies.

Keywords Fine particulate matter . Long-term healtheffects . Urban pollution . Aerosols . Brazil

Introduction

Brazil has a territory of 8.5 million square kilometers and a population of more than 160 million inhabitants, distributedthroughout 26 states. The capitals of those states have beengreatly affected by air pollution. In urban areas, theatmospheric particles that lead to health problems arecommon pollutants. This calls for exhaustive studies of mass concentrations and aerosol composition, becauseelevated concentrations of particulate matter (PM) have been associated with increased morbidity and mortalityfrom cardiovascular and respiratory diseases (Saldiva et al.1994 ; Lanki et al. 2006 ; Stolzel et al. 2007 ; Pope andDockery 2006 ).

Aerosols are introduced into the atmosphere from a variety of anthropogenic sources, including transport,industrial activities, and biomass burning, as well as fromnatural sources, such as volcanic eruptions, sea salt, soildust suspension, and forest fires. Urban pollution isgenerally composed of coarse and fine particulate matter from mineral dust, combustion processes, sulfur dioxide(SO 2 ), nitrogen oxides, ammonia, volatile organic com- pounds (VOCs), and carbon (black and organic). The SO 2 ,ammonia, and nitrogen oxides are precursors of the sulfuric

R. M. de Miranda ( * )School of Arts, Sciences, and Humanities,

University of So Paulo,Rua Arlindo Bttio,1000, Ermelino Matarazzo,CEP 03828-000 So Paulo, Brazile-mail: [email protected]

M. de Fatima Andrade : A. Fornaro : R. AstolfoInstitute of Astronomy, Geophysics, and Atmospheric Sciences,University of So Paulo,So Paulo, Brazil

P. A. de Andre : P. Saldiva University of So Paulo School of Medicine,So Paulo, Brazil

Air Qual Atmos Health (2012) 5:63 77DOI 10.1007/s11869-010-0124-1


2/15

acid, ammonium bisulfate, ammonium sulfate, and ammo-nium nitrate particles that often constitute major fractions of PM 2.5 and PM 10 . Most particulate emissions from combus-tion sources are PM 2.5 mass fractions. Fine particles can bedirectly emitted by sources or produced by condensation,coagulation, or gas-to-particle conversion, the last beingcommon to combustion sources. Detailed descriptions of atmospheric aerosols can be found in the literature (Seinfeldand Pandis 1998 ; Finlayson-Pitts and Pitts 2000 ).

Brazil is unique among countries in that it obtains 70%of its electricity from hydroelectric power plants and usesethanol and gasohol on a broad scale for its light-dutyvehicle fleet. Transportation is responsible for more than60% of all petroleum consumption in Brazil (BEN 2009 ).In urban areas, heavy-duty diesel-powered vehicles are themain source of PM 2.5 , the principal marker of which is black carbon (Sanchz-Ccoyllo et al. 2009 ).

The Brazilian National Ambient Air Quality Standard(NAAQS) sets the 24-h limit for coarse inhalable particu-late matter (PM 2.5 10 ) at 150 g m3 . However, for fine particles (PM 2.5 ) there is no NAAQS (CETESB 2009 ). It isnoteworthy that PM 2.5 can penetrate more deeply into thehuman respiratory system than can PM 2.5 10 , exacerbatingdiseases such as asthma and even inducing heart problems(Saldiva et al. 1994 ; Lanki et al. 2006 ; Stolzel et al. 2007 ).In most urban areas of Brazil, vehicles are considered the principal source of pollutants emitted into the atmosphere.

In Brazil, there are neither established networks nor anycoordinated efforts for the evaluation of PM concentrations.The few data that are available were obtained primarily inisolated, short-term studies conducted in small areas.However, there have been some city-wide and regionalstudies. Orsini et al. ( 1986 ) conducted one of the first andmost important multi-site studies in Brazil, evaluating theconcentrations of particulate matter, between 1982 and 1985,at one natural forest seacoast site and in five urban-industrialcities (Vitria, Salvador, Porto Alegre, So Paulo, and BeloHorizonte). The authors showed that the annual NAAQS for PM10 (50 g m

3 ) was exceeded in the cities of Porto Alegreand So Paulo, whereas the mean PM 2.5 concentrations inBelo Horizonte, Porto Alegre, and So Paulo were 16.5,14.3, and 29.0 g m

3 , respectively. Some authors haveevaluated only PM 10 concentrations, estimates of PM 2.5concentrations being based on the knowledge that PM 2.5typically accounts for 60% of the PM 10 mass (WHO 2006 ).

The objective of the present study was to determineconcentrations of particulate matter, in order to investigatedifferences between summer and winter concentrations,meteorological influences, physicochemical profiles, andthe effects that PM 2.5 concentrations have on human health,in six major Brazilian cities (So Paulo, Rio de Janeiro,Belo Horizonte, Curitiba, Recife, and Porto Alegre), all of which are state capitals.

The Metropolitan Area of So Paulo (MASP) is the richest area in Brazil and is one of the largest megacities in the world,with more than 19 million inhabitants and 6 million vehicles, aswell as a major industrial and technological park. Nevertheless,in recent decades, the economic situation has changed, and theeconomy of the MASP, which previously had a strongindustrial profile, has become one that is focused on servicesand trades. The climate has been influenced by urbanization(Rodriguez et al. 2010 ). Air pollution, high building density,and a lack of green areas, combined with the proliferation of asphalt and concrete surfaces, have resulted in a greater number of urban heat island effects, fewer drizzle events, andrainfall events of greater intensity. The MASP has anextensive air quality monitoring network, which has shownthat ozone levels often exceed the NAAQS limit during springand summer, and that concentrations of inhalable particlesexceed the NAAQS limit mainly during the winter, from Juneto August (CETESB 2009 ). In the winter and summer, the dryand wet seasons, respectively, temperatures range frommoderate (average of 16C in July) to high (average of 28Cin February). Table 1 presents geographic and climaticcharacteristics of the six Brazilian cities studied (Inmet- National Institute of Meteorology- www.inmet.gov.br ). Thehistorical average annual precipitation is 1,465 mm (Table 1),the rainiest months being December, January, and February(>300 mm/month). Studies of air pollution in the MASP have been carried out since the 1970s (Boueres and Orsini 1981 ;Orsini et al. 1986 ; Andrade et al. 1994 ). Such studies havefocused on the characterization of inorganic PM 2.5 and PM 10 .In the MASP, the reported concentrations of PM 10 were oncehigher than were those of PM 2.5 . However, this is no longer the case. The concentrations of PM 10 can still be larger thanthose of PM2.5 , although the difference between both has become smaller than in the past. The concentrations of leaddeclined sharply after the introduction of ethanol (E95, 95%ethanol, 5% water) and gasohol (G80E20, 75% gasoline, 25%ethanol) as fuels. Although the concentration of SO 2 hasdecreased since the 1980s (Fornaro and Gutz 2006 ), the S particulate concentration has maintained approximately thesame value (1 2 g m

3 ). Other, more recent, studies of particulate matter have been performed in the MASP(Castanho and Artaxo 2001 ; Miranda et al. 2002 ; Miranda and Andrade 2004 ). The results of those studies havedemonstrated that aerosol concentrations are higher in thewinter and that vehicle emissions constitute the main source of urban pollutants.

Rio de Janeiro is the second largest metropolis in Braziland is one of the main economic, cultural, and financialcenters in the country. It is also home to the two biggest Brazilian enterprises Petrobrs (a petrochemical company)and Vale (a mining and metallurgical concern). Historicalaverage annual maximum and minimum temperatures are27.2C and 21.0C, respectively (Table 1). July is the

64 Air Qual Atmos Health (2012) 5:63 77
http://www.inmet.gov.br/http://www.inmet.gov.br/


3/15

coldest month and January is the hottest. Summer days arehot and humid, with temperatures above 40C in isolatedareas. In the winter, precipitation is rare and minimumtemperatures are rarely below 10C. Average annual precipitation is 1,140 mm, lower than for any of the other cities profiled here. Between 2003 and 2005, Godoy et al.(2009 ) studied PM 10 and PM2.5 concentrations at tendifferent sites in the city of Rio de Janeiro. The authorsfound that the concentrations of both were low, the lower andupper limits of PM 2.5 concentrations being 7.3 and11.4 g m

3 , respectively. At a site quite near that at whichthe samples analyzed in the present study were collected, thePM 2.5 concentration was 11.1 g m

3 . In that study,anthropogenic sources, such as vehicle emissions and fuelcombustion were found to have made a relatively highcontribution to the fine aerosol mass, accounting for 52 75%.

The greater metropolitan area of Belo Horizonte iscomposed of 34 small cities, with 1.2 million vehicles, locatedin the mineral-rich region of the state of Minas Gerais, wherethere are great quantities of iron ore, manganese, gold, andcalcareous rock. The region also features other important industries related to the metallurgical sector, suchas companiesdealing with non-metallic minerals (cement and lime), petrochemical concerns, and automobile manufacturers. Inaddition, trade, financial services, and public administrationare crucial facets of the local economy. To the east, BeloHorizonte is flanked by the Serra do Curral Mountains(altitude, 1,390 m). Minimum and maximum monthly averagetemperatures are 16.6C and 27.0C, respectively, and theaverage annual precipitation is 1,480 mm (Table 1). In thegreater metropolitan area of Belo Horizonte, where vehicleemissions constitute the main source of air pollution, there isa monitoring network for PM 10 and gases (www.feam.br ). In2006, the annual mean PM 10 concentration was 25.9 g m

3

and was therefore (assuming again that PM 2.5 accounts for 60% of PM 10 ) estimated to be 15.5 g m

3 for PM2.5 .Curitiba is located at a relatively high altitude (924 m)

and has the lowest average annual temperature of any of thecities profiled here. Temperatures can drop to 5C in winter and can rise to 30C in summer. Annual average precipi-

tation is 1,442 mm (Table 1). Curitiba has one of thestrongest economies in the southern region of Brazil, withindustries in many sectors, such as food, furnishings, non-metallic minerals, wood, chemical/pharmaceutical products,and leather, which together account for more than 40% of the local gross domestic product (GDP). Despite havingone of the best public transportation systems of Brazil,Curitiba has the lowest number of passengers per vehicle(Table 2), which can result in more vehicles circulating inthe urban area. According to the most recent air qualityreport for Curitiba ( www.meioambiente.pr.gov.br ), PM10standards were not exceeded in the city in 2008. In thegreater metropolitan area of Curitiba, there are only point events during which the air quality is poor.

The greater metropolitan area of Recife, in comparison withthe other cities profiled here, has the lowest number of inhabitants, the smallest vehicle fleet, and the lowest GDP. Nevertheless, Recife is the most important medical andtechnology hub in the north of Brazil. Approximately 100multinational companies, including Microsoft, Motorola, Bor-land, Inform Air, Oracle, Sun, and Nokia, have headquarters inthe Recife area. It is considered the largest technology park inBrazil, in terms of revenue and number of companies. Tourismalso plays an important role in the local economy. Emissionsfrom ocean-going ships constitute a major source of particulatematter in the atmosphere over the area. The port of Recifehandles an average of 2.2 million tons of cargo annually, andthe main loads are sugar, wheat, corn, barley, malt, fertilizer,slag, and kelp ( http://www.portodorecife.pe.gov.br/doc/COMP-CARG.pdf ). Relative humidity is high throughout the year,and the annual average precipitation is 2,400 mm (Table 1).January is the hottest month, with temperatures above 30C,whereas the lowest temperatures ( 20C) are seen in July. Todate, there has been no air pollution studies conducted inRecife. However, the city has air quality problems related tovehicle emissions, as well as to the emissions from ocean-going ships, which can pose a serious threat but have not beenadequately addressed. The municipal public transport sector has adopted policies aimed at improving the quality of thevehicle fleet and the fuels employed.

Table 1 Geographic and climatic characteristics of the six Brazilian cities evaluated

Characteristic So Paulo Rio de Janeiro Belo Horizonte Porto Alegre Curitiba Recife

Coordinates S 23.5 22.8 19.9 30.0 25.4 8.05W 46.7 43.2 43.9 51.2 49.2 34.9

Altitude (m) 800 Sea level 858 Sea level 934 Sea levelAnnual precipitation (mm) 1,465 1,140 1,481 1,356 1,442 2,418

Annual temperature (C) Min. 15.6 21.0 16.6 15.6 12.5 21Max. 24.9 27.2 27.0 24.8 23.1 29Mean 19.3 25.0 21.1 19.5 16.9 25

Source: Instituto Nacional de Meteorologia (INMET, National Meteorology Institute)

Air Qual Atmos Health (2012) 5:63 77 65
http://www.feam.br/http://www.meioambiente.pr.gov.br/http://www.portodorecife.pe.gov.br/doc/COMPCARG.pdfhttp://www.portodorecife.pe.gov.br/doc/COMPCARG.pdfhttp://www.portodorecife.pe.gov.br/doc/COMPCARG.pdfhttp://www.portodorecife.pe.gov.br/doc/COMPCARG.pdfhttp://www.meioambiente.pr.gov.br/http://www.feam.br/


4/15

The greater metropolitan area of Porto Alegre has morethan 4 million inhabitants and 0.6 million vehicles. It is therichest city in the south of Brazil and is second only to theMASP in terms of agricultural production, industrial output,and GDP (Table 2). The Porto Alegre area features severalmajor industries that emit atmospheric pollutants, including anoil refinery, two metallurgical plants, a petrochemical indus-trial complex, and two large coal-fired power plants. The area is located at sea level, and the climate is therefore stronglyinfluenced by cold air masses migrating from Polar Regions.Temperatures are high in the summer and low in the winter,the monthly average maximum and minimum temperatures being 24.8C and 15.6C, respectively (Table 1). Althoughannual average precipitation is 1,356 mm, there is no dry or wet season in Porto Alegre, where the monthly averagerainfall rate ranges from 80 to 137 mm. Studies of particulatematter was conducted from October 2001 to December 2002in Porto Alegre (Braga et al. 2005 ; Dallarosa et al. 2008 ). At a sampling site near that chosen for the present study, onlyPM10 concentrations were measured, and the annual meanwas found to be 28 g m

3 . Again considering that PM 2.5accounts for 60% of the PM 10 mass, the annual mean PM 2.5concentration was estimated at 16.8 g m

3 . The authorsshowed that the main emission sources in the area werevehicle emissions and industrial activities, both of which areassociated with the presence of S and of the metals Cu, Zn, Ni, V, and Cr.

The present studyis thefirst to carry out long-termsamplingof PM10 and PM 2.5 concentrations in multiple cities in Brazil.

Methodology

Study design

In a collaborative effort involving several Brazilian institutionsand coordinated by the University of So Paulo School of Medicine,urbanatmospheric aerosol PM 2.5 were sampled over a period of more than 1 year (June 2007 to August 2008) insix large cities within Brazil. The samples were analyzed for mass concentration and trace element composition. Weemployed gravimetry to determine PM 2.5 mass concentrations,optical reflectance to quantify black carbon concentrations,energy dispersive X-ray fluorescence (EDXRF) to characterizeelemental composition, and ion chromatography to determinethe composition and concentrations of anions and cations.

Sampling sites

As can be seen in Fig. 1, the sampling sites were located insix state capitals: So Paulo, Rio de Janeiro, BeloHorizonte, Curitiba, Recife, and Porto Alegre. The siteswere within central regions of the metropolitan areas, wherethe traffic volumes were high and the traffic was composedmainly of light-duty (gasohol- and ethanol-powered)vehicles and heavy-duty (diesel-powered) buses. Table 1shows the geographic information and climatological data for the sampling sites. Population, GDP, and vehicle fleet data for each of the cities and for their greater metropolitanareas are shown in Table 2.

Table 2 Population and vehicle fleet data of capital-cities and metropolitan areas (IBGE 2009 and 2008)

Characteristic So Paulo Rio de Janeiro Belo Horizonte Porto Alegre Curitiba Recife

Population (millions)City 11.0 6.18 2.45 1.44 1.85 1.56

GMA 19.4 11.1 5.01 4.10 3.42 3.26GDP per capita (US$1,000) 14.7 11.5 7.9 11.8 10.5 6.8

Total vehicle fleet (millions)City 5.46 1.74 1.06 0.59 1.06 0.38GMA 6.14 1.95 1.23 0.67 1.19 0.45

City vehicle fleet (million)Light-duty a 4.25 1.45 0.80 0.47 0.82 0.28

Motorcycle b 0.68 0.16 0.14 0.07 0.12 0.06Heavy-duty c 0.53 0.13 0.12 0.05 0.12 0.04

Passengers/vehicleCity 2.6 4.3 3.1 3.0 2.3 5.5GMA 3.2 5.7 4.1 6.1 2.9 7.2

GMA greater metropolitan area a Burning ethanol or gasohol (25% ethanol+75% gasoline) b Burning gasoholc Burning diesel



5/15

Sample collection

We collected 24-h particle samples using a PM 2.5 HarvardImpactor, developed at the Harvard School of PublicH e al t h. T he d ev i ce w a s e qu ip pe d w it h 3 7- m m polycarbonate filters (0.8 m pore size) for PM 2.5 . Thesampler is composed of a vacuum pump with a system tomonitor and regulate flow rate (10 Lmin

1 ), a time counter and an impactor that allows only material with anaerodynamic diameter


6/15

lytic conditions for anion determination were as follows: a 2504 mm anion column (Metrosep A-Supp 5; Metrohm),eluent solution (4.0 mmol L

1 Na 2 CO 3 /1.0 mmol L1

NaHCO 3 ), flow of 0.7 mL min1 , a suppressor column

(Metrohm), and regenerative solution (50 mmol L1 H2 SO4 ).

Analytic conditions for cation determination were as follows:a 1504 mm cation column (Metrosep C2-150; Metrohm),eluent solution (4 mmol L

1 tartaric acid/0.75 mmol L1

dipicolinic acid), flow of 1.0 mL min1 , and an electronic

suppression system (Metrohm). The analytical determinationof each major ion was made using a calibration plot with a concentration range of 5 50 mol L

1 .

Impact on mortality

To calculate the expected number of deaths attributable toair pollution, we modified the approach proposed by Ostro(2006 ), applying the following equation:

E b D C =10

where E is the expected number of premature deaths due tolong-term exposure, beta is the percentage change inmortality per 10 g m

3 change in annual concentrationof PM 2.5 , D is the number of natural deaths in the age groupover 45 years of age, and C is the annual concentration of PM 2.5 above the World Health Organization (WHO) annualair quality guideline (10 g m

3 ).Based on Ostro ( 2006 ), we assumed a coefficient of

0.06, indicating that in a long-term exposure, an excess of 10 g m

3 of PM 2.5 is associated with a 6% increase in all-cause mortality in adults. Data on natural deaths in adultsover 45 years were obtained from the Brazilian NationalMinistry of Health (Datasus et al. 2008 ) which currentlyuses the tenth revision of the International Classification of Diseases to categorize cause of death. Results are expressedas the percentage increase in the number of deaths as wellas the number of deaths attributable to air pollution.

Results and discussion

Table 3 shows the number of samples collected during thestudy period, the mean PM 2.5 concentrations, the mean

black carbon concentrations, and the proportion of PM 2.5represented by black carbon for each city and for the study period as a whole. The concentrations of PM 2.5 werehighest in So Paulo, which was followed by Rio deJaneiro, Belo Horizonte, Curitiba, Porto Alegre, and Recife.However, the same was not true for the concentrations of black carbon and its percentage contribution to PM 2.5 . It isof note that the percentage contribution of black carbon waslowest in Rio de Janeiro, probably because diesel-powered

(heavy-duty) vehicles represent a smaller proportion of thetotal vehicle fleet in Rio de Janeiro than in the other evaluated cities. Likewise, Curitiba and Belo Horizonte,which have similar proportions of heavy-duty vehicles intheir fleets, showed comparable percentage contributions of black carbon (Table 2).

In Rio de Janeiro vehicles pollutant emissions inspectionare taking place since 1997 ( www.inea.rj.gov.br ), while for So Paulo the inspection started in 2009 and it does not applied in the other cities. For a study performed in sixEuropean cities (Silanpaa et al. 2006 ), black carbonconcentrations were all below 10% being the same for

PM 2.5 ( g m3 ) BC ( g m

3 ) %BCCity N Mean (SD) Mean (SD) Mean (SD)

So Paulo 340 28.1 (13.6) 10.6 (6.4) 38 (14)Rio 427 17.2 (11.2) 3.4 (2.5) 20 (7)Belo Horizonte 371 14.7 (7.7) 4.5 (3.3) 31 (13)

Curitiba 320 14.4 (9.5) 4.4 (4.0) 30 (11)

Porto Alegre 342 13.4 (9.9) 3.9 (4.3) 26 (11)Recife 327 7.3 (3.1) 1.9 (1.1) 26 (12)

Table 3 Mean concentrationsof PM 2.5 and black carbon,together with the percentagecontribution of BC, in the sixBrazilian cities studied

N total number of samples

Table 4 Concentrations of trace elements a , in the summer and winter,in the ambient air of urban areas around the world

City/region PM 2.5 concentrations( g m

3 )

Summer Winter

So Paulo, Brazil (this study) 23.1 35.5Rio de Janeiro, Brazil (this study) 15.8 23.0Belo Horizonte, Brazil (this study) 14.5 18.5Porto Alegre, Brazil (this study) 13.9 19.3Curitiba, Brazil (this study) 13.3 18.1

Recife, Brazil (this study) 10.5 12.5Beijing, China (Zhao et al. 2009 ) 85.8 91.1Yokohama, Japan (Khan et al. 2010 ) 20.8 21.1Agra, India (Kulshrestha et al. 2009 ) 64.2 144.2Zonguldak, Turkey (Akyuz and abuk 2009 ) 32.4 83.3

United States a (Bell et al. 2007 ) 16.2 13.9Helsinki, Finland (Sillanpaa et al. 2000 ) 12.1 10.2

a Multi-city study

http://www.inea.rj.gov.br/http://www.inea.rj.gov.br/


7/15

USA (Bell et al. 2007 ). But higher percentages wereobserved in Helsinki, with 14% of PM 2.5 being explained by the BC (Viidanoja et al. 2002 ) and 43% for Paris(Ruellan and Cachier 2001 ). Both studies were performedin urban areas. The high percentages found for the sixBrazilian cities are not unusual in Brazil previous studiesfound values around 30% of BC in the PM 2.5 (Castanhoand Artaxo 2001 ; Miranda and Tomaz 2008 ; Godoy et al.2009 ).

Because of the great number of samples, together withthe variations in the concentrations related to the sourcesand to changes in meteorological conditions over the study period, the standard deviations were high.

For comparison purposes, Table 4 presents the PM 2.5concentrations in the ambient air of urban areas in variouscities and regions around the world (Zhao et al. 2009 ; Khanet al. 2010 ; Kulshrestha et al. 2009 ; Akyuz and abuk 2009 ; Bell et al. 2007 ; Sillanpaa et al. 2000 ). In the

0

10

20

30

40

50

60

70

80

C o n c e n

t r a

t i o n

( g . m

- 3 )

C o n c e n

t r a

t i o n

( g . m

- 3 )

So Paulo PM2.5 Black carbon

0

10

20

30

40

50

60

70

06/07/07 07/27/07 09/15/07 11/04/07 12/24/07 02/12/08 04/02/08 05/22/08 07/11/08 08/30/08

04/28/07 06/17/07 08/06/07 09/25/07 11/14/07 01/03/08 02/22/08 04/12/08 06/01/08 07/21/08 09/09/08

Rio de Janeiro PM2.5 Black carbon

Fig. 2 Seasonal distribution for PM 2.5 and black carbon in micrograms per cubic meter for So Paulo, Rio de Janeiro, Belo Horizonte, Curitiba,Porto Alegre, and Recife



8/15

Brazilian cities studied here, PM 2.5 concentrations weregenerally lower than those reported for other major cities, interms of the annual average values.

Seasonal variation

Figure 2 displays the seasonal variations in PM 2.5 and black carbon concent rat ions for all six cit ies evaluated.In general, concentrations were lower in the summer months (November to February), the exception being

Recife, where concentrations remained low throughout most of the year. It is difficult to compare concentrationsamong cities because there are numerous variables, suchas the total number of vehicles, the proportions of vehicles burning the various types of fuels (Table 2),and meteorological parameters (Table 1). Nevertheless,it is clear that concentrations of PM 2.5 and black carbonwere highest in So Paulo (Table 3).

In some countries in the North Hemisphere, the differ-ences between winter and summer PM concentrations are

0

10

20

30

40

50

60Belo Horizonte PM2.5 Black carbon

0

10

20

30

40

50

60

05/03/07 06/22/07 08/11/07 09/30/07 11/19/07 01/08/08 02/27/08 04/17/08 06/06/08 07/26/08

04/28/07 06/17/07 08/06/07 09/25/07 11/14/07 01/03/08 02/22/08 04/12/08 06/01/08 07/21/08 09/09/08

Curitiba PM2.5 Black carbon

C o n c e n

t r a

t i o n

( g . m

- 3 )

C o n c e n

t r a

t i o n

( g . m

- 3 )

Fig. 2 (continued)



9/15

due to home heating, changes in the number of cars on thestreets, and other seasonal characteristics (Akyuz andabuk 2009 ). It is of note that, in the cities evaluated in the present study, with the possible exception of Porto Alegre,there are no extreme seasonal variations in human behavior and activities. Therefore, although meteorological conditions(temperature, relative humidity, wind speed, and winddirection) seem to have influenced PM 2.5 concentrations inthe six capitals, other characteristics typically remained thesame throughout the year and had no seasonal effects.

Meteorological conditions

Meteorological conditions had a great influence on PM 2.5concentrations. Table 5 presents 3-month averages for temperature, relative humidity, wind speed, PM 2.5 concen-trations, black carbon concentrations, and accumulated precipitation, as well as showing the number of measure-ments taken.

Although there was little variation in wind speed, therewas considerable variation among the cities in terms of

0

10

20

30

40

50

60Porto Alegre PM2.5 Black carbon

0

5

10

15

20

25

30

05/28/07 07/17/07 09/05/07 10/25/07 12/14/07 02/02/08 03/23/08 05/12/08 07/01/08 08/20/08

05/28/07 07/17/07 09/05/07 10/25/07 12/14/07 02/02/08 03/23/08 05/12/08 07/01/08 08/20/08

Recife PM2.5 Black carbon

C o n c e n

t r a

t i o n

( g . m

- 3 )

C o n c e n

t r a

t i o n

( g . m

- 3 )

8/20/088/20/088/20/08

Fig. 2 (continued)



10/15

Table 5 Three-month averages for temperature, relative humidity, wind speed, PM 2.5 , and black carbon, together with 3-month accumulated precipitation values

City (period) a Temperature (C) Relativehumidity (%)

Wind speed(m s

1 ) N Accum.

prec. (mm)PM 2.5 ( g m

3 ) N BC ( g m3 ) N

SP (Jun Aug) 17.5 (3.1) 67 (12) 2.0 (0.6) 81 150.4 34.3 (13.8) 71 12.2 (6.4) 71SP (Sept Nov) 20.3 (2.7) 70 (10) 2.3 (0.7) 91 331.0 27.1 (11.5) 66 8.1 (4.6) 62

SP (Dec Feb) 21.9 (2.3) 74 (9) 2.2 (0.5) 91 750.6 19.9 (7.5) 67 7.8 (3.3) 71SP (Mar May) 19.9 (2.8) 71 (10) 2.1 (0.6) 92 342.4 24.7 (11.5) 78 10.8 (6.5) 88SP (Jun Aug) 17.5 (2.5) 69 (12) 1.8 (0.7) 91 146.2 35.7 (16.4) 58 13.8 (8.1) 67RJ (Jun Aug) 21.2 (2.4) 78 (10) 2.3 (0.7) 92 41.6 19.8 (12.4) 111 4.0 (2.5) 103RJ (Sept Nov) 23.5 (2.2) 78 (10) 2.4 (0.7) 88 272.2 14.7 (4.2) 72 2.4 (1.5) 72RJ (Dec Feb) 25.4 (2.4) 83 (10) 2.1 (0.6) 91 560.8 12.4 (5.5) 71 2.1 (1.2) 76RJ (Mar May) 23.5 (2.6) 85 (8) 1.9 (0.6) 91 364.2 17.3 (12.7) 84 3.6 (2.3) 83RJ (Jun Aug) 21.1 (2.3) 80 (10) 2.0 (0.8) 92 139.2 19.9 (12.6) 89 4.4 (3.2) 91BH (Jun Aug) 19.4 (1.8) 55 (8) 2.6 (0.8) 92 5.6 17.7 (7.7) 109 5.7 (3.9) 109BH (Sept Nov) 22.4 (2.3) 54 (11) 3.1 (0.9) 88 247.6 17.6 (8.0) 58 3.5 (2.0) 77BH (Dec Feb) 22.5 (1.7) 67 (11) 2.4 (0.8) 91 800.4 9.0 (2.8) 75 2.9 (1.7) 87BH (Mar May) 21.5 (1.7) 64 (9) 2.0 (0.7) 92 312.2 13.7 (6.6) 75 5.1 (3.2) 88

BH (Jun Aug) NA NA NA NA NA 14.8 (8.5) 54 5.1 (4.4) 56CUR (Jun Aug) 14.6 (3.6) 72 (10) 2.4 (0.6) 76 9.6 18.1 (9.0) 27 5.0 (4.1) 28CUR (Sept Nov) 18.3 (2.6) 75 (8) 2.6 (0.7) 88 351.2 14.5 (8.0) 75 3.3 (1.8) 75CUR (Dec Feb) 20.3 (2.1) 78 (6) 2.4 (0.6) 91 549.8 8.3 (4.7) 51 2.1 (1.2) 77CUR (Mar May) 17.4 (3.3) 78 (9) 2.0 (0.8) 71 380.8 14.5 (10.1) 80 5.2 (4.9) 85CUR (Jun Aug) 15.0 (2.9) 75 (11) 1.9 (0.8) 92 297.2 16.7 (10.7) 87 6.2 (4.8) 92PA (Jun Aug) 13.5 (3.8) 79 (11) 1.6 (0.7) 92 568.6 18.3 (10.7) 78 5.7 (5.4) 76PA (Sept Nov) 19.9 (2.8) 74 (10) 1.8 (0.7) 89 325.2 13.8 (9.8) 62 3.4 (3.5) 78PA (Dec Feb) 24.0 (2.0) 70 (8) 1.8 (0.5) 89 258.4 8.0 (4.2) 60 1.8 (0.8) 82PA (Mar May) 20.0 (4.2) 74 (8) 1.4 (0.6) 92 427.6 15.7 (10.2) 74 5.2 (4.7) 83PA (Jun Aug) 14.8 (3.0) 80 (9) 1.4 (0.7) 92 393.2 9.5 (9.0) 68 3.4 (4.2) 76RE (Jun Aug) 23.8 (0.7) 81 (4) NA 81 832.0 8.7 (3.7) 69 2.4 (1.1) 67RE (Sept Nov) 25.2 (1.1) 72 (5) 2.8 (0.4) 91 185.0 6.9 (3.1) 86 1.5 (0.9) 84RE (Dec Feb) 26.7 (0.7) 71 (4) 2.6 (0.4) 91 148.6 6.8 (2.6) 75 1.4 (0.6) 89RE (Mar May) 25.4 (1.2) 82 (6) 1.6 (0.4) 85 1,029.8 6.9 (2.1) 53 2.1 (1.0) 65RE (Jun Aug) NA NA NA NA NA 7.2 (2.9) 44 2.8 (1.0) 58

N total number of measurements, Accum. prec. accumulated precipitation, SP So Paulo, RJ Rio de Janeiro, BH Belo Horizonte, CUR Curitiba, PAPorto Alegre, RE Recifea Data collected from June 2007 to August 2008

Ion Concentrat ion ( g m3 )

So Paulo Rio de Janeiro Belo Horizonte Porto Alegre Curit iba RecifeMean (SD) Mean (SD) Mean (SD) Mean (SD) Mean (SD) Mean (SD)

Cl

0.24 (0.29) 0.11 (0.25) 0.04 (0.03) 0.15 (0.17) 0.07 (0.15) 0.15 (0.16) NO3

1.22 (1.09) 0.56 (0.80) 0.19 (0.13) 0.43 (0.43) 0.16 (0.13) 0.11 (0.06)

SO 42 3.09 (1.98) 1.91 (1.41) 1.15 (0.51) 1.17 (0.91) 1.08 (0.68) 0.61 (0.33)

Na + 0.24 (0.16) 0.21 (0.11) 0.08 (0.04) 0.19 (0.11) 0.10 (0.05) 0.33 (0.14) NH4

+ 1.25 (0.89) 0.80 (0.78) 0.34 (0.19) 0.35 (0.36) 0.37 (0.27) 0.18 (0.13)

Table 6 Average concentrationsof anions and cations in the sixBrazilian cities evaluated



11/15

temperature, relative humidity, and accumulated precipita-tion. In So Paulo, Rio de Janeiro, Belo Horizonte, andCuritiba, the winter is characterized by lower temperatures,lower relative humidity, and less precipitation, resulting inhigher concentrations of PM 2.5 and black carbon. Asexpected, we observed a strong negative correlation between PM 2.5 concentrations and accumulated precipita-tion. Precipitation can remove particles by rainout (in-cloudremoval) and washout (below-cloud removal), as well as preventing the resuspension of soil dust (Seinfeld andPandis 1998 ), thereby decreasing PM concentrations. InPorto Alegre and Recife, the situation is somewhat different. In Porto Alegre, precipitation is evenly distributedthroughout the year, with only a slight increase during thewinter months (June to September), concentrations of PM 2.5 and black carbon being lower between October andFebruary, when there is slightly less precipitation. InRecife, precipitation is heavy from March until July, andthere is little variation in PM 2.5 concentrations over thecourse of the year.

In So Paulo, relative humidity is lower in the winter months, as are wind speed and precipitation, winter beingthe dry season. Winter thermal inversions create conditionsthan can elevate pollutant concentrations, and winter conditions in general promote higher PM 2.5 concentrations.The great number of tall buildings, combined with the proliferation of asphalt and concrete surfaces, can createurban heat island effects, which can also influence pollutant concentrations and other environmental conditions. In thesummer months, concentrations are lower, because of thegreat number of low pressure systems, which favor pollutant dispersion.

We had expected to find a negative correlation betweenwind speed and PM 2.5 concentrations, higher wind speedsresulting lower concentrations. However, we observed nosuch correlation for any of the six cities evaluated. Other authors have stated that this correlation is dependent on localsources and particle size (Khan et al. 2010 ; Tai et al. 2010 ).

Ion composition

The concentrations of major ions in PM 2.5 are shown inTable 6. As expected, the concentrations of all ions werehighest in So Paulo. In particular, we observed highconcentrations of ammonium, which can be associated withvehicle emissions (reduction reactions in three-way catalyticconverters) and biogenic emissions (Fontenele et al. 2009 ).In all six cities, the concentrations of sulfate were higher than were those of any other ion. The lowest sodiumconcentrations were observed in Belo Horizonte andCuritiba, which are farther from the ocean than are theother metropolitan areas. Comparing the element S, asanalyzed by EDXRF, with the S present in sulfate (SO 4

2 ),

as analyzed by ion chromatography, we observed that all Sin PM 2.5 was in sulfate form. As can be seen in Fig. 3, thiscorrelation was significant ( R=0.9822, angular coefficient=1.004 and linear coefficient=0.008).

Elemental composition

Table 7 presents the mean values obtained in the EDXRFanalysis for the summer months (October to March) andwinter months (April to September), as well as the totalnumbers of samples analyzed. As previously mentioned,concentrations were higher during the winter.

Soil-derived elements presented high concentrations,although anthropogenic elements also appeared. Vanadiumis produced by the burning of diesel and oil, whereas Sderives from combustion processes. The latter is present insulfate form from SO 2 oxidation (Fig. 3). Gaseous SO 2 is a product of fossil fuel combustion (Seinfeld and Pandis1998 ). Manganese can be related to industrial processes,whereas Fe and Zn, in the fine fraction, are associated withcombustion processes (Castanho and Artaxo 2001 ; Geller et al. 2006 ). Cu and Zn are commonly added to engine oils for their antioxidant properties (Ferreira da Silva et al. 2010 ).Potassium has been associated with biomass burning (Kunduet al. 2010 ). In cities located in southern Brazil, such asCuritiba and Porto Alegre, concentrations of K can also beassociated with wood burning for heat during the winter.

With the exceptions of Si, S, K, Fe, and Zn, all of theelements showed similar concentrations among the sixcities. Belo Horizonte is situated in a mining region, and the prevailing winds are from the ESE (from the mines andtoward the city), which could explain the high concen-trations of Si, Ca, Mn, and Fe, those of S being attributed to

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.40.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

S E D X f l u o r e s c e n c e

( n u m

b e r o

f m o

l s . m

- 3 )

Sionic chromatography

(number of mols.m -3)

Fig. 3 Relation between sulfur (number of moles per cubic meter)measured by EDX analysis and sulfate by ionic chromatography



12/15

T a

b l e 7

C o n c e n t r a t i o n s o f P M

2 . 5 ,

B C

, a n d t r a c e e l e m e n t s s u b m i t t e d t o E D X R F a n a l y s i s a

, f o r t h e s u m m e r m o n t h s ( O c t o b e r t o M a r c h ) a n d w i n t e r m o n t h s ( A p r i l t o S e p t e m b e r )

E l e m e n t S a o P a u l o ( n = 2 0 7 )

b

R i o d e J a n e i r o ( n = 1 5 0 )

b

B e l o H o r i z o n t e ( n = 2 1 7 )

b

P o r t o A l e g r e ( n = 1 7 4 )

b

C u r i t i b a ( n = 1 5 5 )

b

R e c i f e ( n = 1 4 8 )

b

S u m m e r

W i n t e r

S u m m e r

W i n t e r

S u m m e r

W i n t e r

S u m m e r

W i n t e r

S u m m e r

W i n t e r

S u m m e r

W i n t e r

M e a n S D

M e a n S D

M e a n S D

M e a n S D

M e a n S D

M e a n S D

M e a n S D

M e a n S D

M e a n S D

M e a n S D

M e a n S D

M e a n S D

P M 2

. 5 c

2 3 . 1

9 . 5

3 5 . 5

1 4 . 5

1 5 . 8

5 . 5

2 3 . 0

1 3 . 0

1 4 . 5

6 . 5

1 8 . 5

7 . 5

1 3 . 9

6 . 7

1 9 . 3

1 4 . 3

1 3 . 3

5 . 1

1 8 . 1

9 . 2

1 0 . 5

2 . 8

1 2 . 5

4 . 6

B C c

7 . 1

3 . 4

1 3 . 1

7 . 3

2 . 3

1 . 2

4 . 0

2 . 5

3 . 4

1 . 7

5 . 8

4 . 4

2 . 6

2 . 4

4 . 9

5 . 0

2 . 7

1 . 4

5 . 0

4 . 1

1 . 3

0 . 6

2 . 7

1 . 1

A l

3 3 . 2

2 8 . 4

7 5 . 5

7 3 . 4

3 2 . 9

4 1 . 2

6 2 . 3

6 6 . 5

4 3 . 9

5 5 . 8

5 4 . 0

3 9 . 5

2 8 . 3

2 4 . 3

5 0 . 6

5 2 . 4

4 8 . 4

8 1 . 9

5 5 . 6

5 6 . 8

5 5 . 4

8 3 . 3

2 6 . 9

2 9 . 0

S i

7 6 . 6

4 9 . 9

1 9 6 . 7 1 4 9 . 1

7 7 . 9

6 3 . 0

1 5 3 . 3 1 5 3 . 7 1 6 2 . 4 1 2 6 . 5 2 2 3 . 5 1 3 3 . 6

7 4 . 8

6 0 . 6

8 6 . 0 1 1 1 . 7

7 4 . 3

7 4 . 4

1 0 1 . 8 1 0 3 . 8 1 3 9

. 7 1 6 3 . 8

5 8 . 5

3 9 . 5

P

3 6 . 4

1 4 7 . 9

2 4 . 3

1 7 . 6

9 . 5

4 8 . 6

4 . 5

4 . 0

7 . 1

8 . 2

1 2 . 0

4 3 . 8

5 . 4

5 . 2

4 . 8

5 . 1

1 . 9

1 . 5

3 . 1

3 . 5

S

8 9 6 . 6 4 7 2 . 0 9 6 7 . 1 5 4 4 . 8 6 3 8 . 2 3 9 8 . 8 6 7 2 . 9 4 8 5 . 3 3 3 1 . 5 1 9 5 . 0 3 8 8 . 9 1 8 1 . 6 3 8 9 . 0 3 4 8 . 4 3 3 9 . 0 2 9 5 . 5 3 8 4 . 0 2 6 0 . 3 3 9 0 . 5 2 6 3 . 7 2 2 8

. 6 1 0 4 . 5 1 6 5 . 5 1 0 7 . 8

C l

3 3 . 5

4 9 . 5

1 6 1 . 2 2 0 2 . 4

1 8 . 8

5 4 . 6

9 1 . 6

1 4 2 . 8

1 1 . 1

3 4 . 8

1 0 . 0

1 5 . 9

5 4 . 5

8 0 . 5

1 1 4 . 3 1 9 3 . 5

4 1 . 8

1 8 9 . 9

2 4 . 3

5 5 . 7

5 8 . 8

6 4 . 6

1 7 6 . 8 1 7 7 . 7

K

1 3 7 . 6 1 3 0 . 1 3 6 3 . 5 2 2 1 . 0 1 2 4 . 9 2 8 1 . 0 2 4 1 . 4 1 8 7 . 7 1 4 1 . 2 1 4 0 . 4 2 2 4 . 9 1 3 1 . 7 1 5 8 . 5 1 5 6 . 7 3 2 2 . 3 3 2 0 . 2 1 5 4 . 6 1 5 1 . 4 3 5 6 . 5 2 8 9 . 4 1 1 7

. 4

7 7 . 7

1 5 8 . 9 1 9 3 . 2

C a

5 2 . 7

2 6 . 6

1 1 9 . 5 1 0 8 . 8

3 1 . 9

2 3 . 7

5 3 . 7

6 2 . 8

9 8 . 5

7 2 . 5

9 6 . 2

5 2 . 8

3 5 . 8

2 2 . 5

3 8 . 3

4 0 . 6

3 7 . 0

3 5 . 0

4 9 . 3

5 3 . 3

5 3 . 5

2 9 . 9

5 3 . 7

4 0 . 5

T i

5 . 7

3 . 2

1 3 . 1

1 0 . 5

4 . 4

3 . 5

6 . 5

5 . 4

4 . 4

4 . 1

5 . 4

3 . 2

3 . 7

2 . 7

5 . 2

6 . 0

4 . 0

3 . 8

5 . 1

4 . 9

4 . 0

5 . 2

2 . 2

1 . 0

V

1 . 3

0 . 9

2 . 0

1 . 5

4 . 9

2 . 9

3 . 8

2 . 2

1 . 5

0 . 8

1 . 6

0 . 8

0 . 9

0 . 5

1 . 5

1 . 0

0 . 7

0 . 6

0 . 7

0 . 9

0 . 4

0 . 3

0 . 3

0 . 2

C r

1 . 0

1 . 0

2 . 1

1 . 8

1 . 8

0 . 7

1 . 8

0 . 7

0 . 7

0 . 9

0 . 6

0 . 6

1 . 3

0 . 7

1 2 . 1

6 9 . 1

0 . 7

0 . 6

0 . 8

0 . 8

0 . 4

0 . 4

0 . 5

0 . 5

M n

6 . 6

2 1 . 2

8 . 2

5 . 0

3 . 7

1 . 9

5 . 0

3 . 4

2 5 . 2

6 0 . 5

4 9 . 2

4 4 . 9

2 . 8

2 . 8

6 . 2

1 9 . 7

2 . 1

4 . 4

2 . 5

2 . 2

1 . 3

1 . 3

2 . 9

3 . 4

F e

1 2 8 . 3

5 7 . 2

2 4 7 . 9 1 4 2 . 6

5 6 . 3

2 6 . 4

8 9 . 5

6 5 . 9

1 0 7 . 9

8 0 . 6

1 4 2 . 6

7 7 . 7

6 0 . 2

4 4 . 9

9 0 . 7 1 3 4 . 3

5 7 . 3

3 1 . 7

8 2 . 9

5 9 . 9

5 0 . 7

4 6 . 7

7 4 . 4

5 2 . 4

N i

0 . 9

1 . 5

1 . 2

1 . 1

3 . 6

1 . 5

2 . 2

1 . 4

0 . 8

0 . 5

0 . 8

0 . 6

0 . 7

0 . 2

4 . 7

1 3 . 9

0 . 4

0 . 5

0 . 9

0 . 8

0 . 3

0 . 3

0 . 4

0 . 5

C u

8 . 1

1 3 . 8

1 3 . 1

9 . 8

7 . 7

4 . 8

9 . 9

1 0 . 7

4 . 1

6 . 4

2 . 5

3 . 8

1 . 9

1 . 8

5 . 2

7 . 5

2 . 6

4 . 2

3 . 8

4 . 8

0 . 9

0 . 9

1 . 8

1 . 8

Z n

5 3 . 6

4 1 . 1

9 9 . 0

9 6 . 1

1 8 . 1

1 5 . 9

3 0 . 9

2 6 . 5

1 2 . 6

1 2 . 3

1 6 . 1

1 3 . 5

1 2 . 4

1 8 . 9

1 8 . 7

2 0 . 2

9 . 0

1 0 . 6

2 5 . 8

4 6 . 1

8 . 9

1 0 . 9

2 4 . 9

2 8 . 9

S e

3 . 3

3 . 6

2 3 . 4

5 2 . 1

0 . 4

0 . 5

0 . 4

0 . 3

0 . 8

0 . 3

2 . 5

3 . 1

0 . 4

0 . 6

0 . 4

0 . 4

1 . 1

1 . 1

1 . 3

1 . 7

B r

2 . 8

2 . 7

7 . 8

9 . 4

4 . 5

2 . 1

7 . 8

4 . 9

2 . 5

1 . 8

2 . 4

1 . 6

2 . 4

1 . 3

3 . 4

2 . 6

2 . 0

1 . 5

3 . 5

3 . 3

2 . 9

1 . 5

3 . 5

3 . 1

P b

1 5 . 4

1 2 . 6

2 2 . 4

2 0 . 5

9 . 5

7 . 1

1 4 . 1

1 1 . 4

3 . 0

4 . 8

6 . 7

8 . 1

3 . 2

3 . 1

5 . 4

5 . 4

7 . 9

1 5 . 8

1 1 . 5

1 6 . 2

2 . 1

1 . 9

4 . 5

4 . 6

a

C o n c e n t r a t i o n s i n n a n o g r a m s p e r c u b i c m e t e r , u

n l e s s o t h e r w i s e n o t e d

b

N u m b e r o f s a m p l e s f o r w h i c h e l e m e n t a l a n a l y s i s d a t a w e r e a v a i l a b l e f o r t h e e n t i r e s t u d y p e r i o d

c

C o n c e n t r a t i o n s i n m i c r o g r a m s p e r c u b i c m e t e r



13/15

vehicle emissions. In Rio de Janeiro, the concentrations of S might also be related to the fact that there are oilrefineries near the sampling site. However, wind directionanalysis showed that the highest S concentrations werelinked to wind coming not only from the direction of therefineries but also from urban areas. Therefore, S cannot beattributed to one source alone.

As can be seen in Table 5, the concentrations of K werehighest in Curitiba, followed by Porto Alegre, So Paulo,Belo Horizonte, Rio de Janeiro, and Recife. However, it islikely that besides the biomass burning, biogenic sourcescan contribute to the concentrations of K, includingmaterials such as pollen, spore, fragments of animals and plants, bacteria, algae, and fungi (Miranda et al. 2002 ).

So Paulo has the largest vehicle fleet and the highest vehicle emissions, which explains the high concentrationsof S, Fe, and Zn. The average elemental compositionsobserved for all six Brazilian cities were higher than thosereported for other cities in the US, for most of the elements(Table 8). On the other hand, concentrations were lower comparing to Hanoi in Vietnam (Cohen et al. 2010 ), in a sample area with intense sources due to coal thermoelectric power generation, but comparable to other studies aroundurban areas in Brazil (Dallarosa et al. 2008 ; Castanho andArtaxo 2001 ; Miranda and Tomaz 2008 )

In a companion study (Andrade et al. 2010 ), principalcomponent analysis was applied in order to evaluate thecontribution of each source to the PM 2.5 mass, with special

emphasis on the identification of the contribution made byvehicle emissions.

Table 9 shows, for each city, the annual mean PM 2.5concentration, the portion that exceeded the safe levelrecommended by the WHO, the annual mortality rate, andthe estimated number of deaths associated with the excessPM 2.5 . The annual mean PM 2.5 concentrations exceeded thesafe level in So Paulo, Rio de Janeiro, Belo Horizonte,Porto Alegre, and Curitiba, although not in Recife. SoPaulo presented the worst results, in function of its larger population and higher PM 2.5 concentrations, So Paulo

Element Thisstudy a

Annual mean concentration (ng m3 )

USA (nationwide)(Bell et al. 2007 )

Atlanta, GA, USA(Liu et al. 2005 )

Birmingham, AL, USA(Liu et al. 2005 )

Hanoi, Vietnam(Cohen et al. 2010 )

Al 43.9 29.2 22 21 224Si 125.3 105 89 66 1,080P 10.9 4.8 39S 496.6 3,698 b 3,810

Cl 66.6 24.8 292K 225.3 72.9 59 118 974Ca 64.0 57 43 114 459Ti 5.5 5.33 31

V 1.53 5.64 3Cr 1.40 2.03 5Mn 11.71 3.00 2 15 61Fe 108.3 85.7 74 178 394 Ni 1.00 1.85 4

Cu 4.42 3.98 3 7 10

Zn 29.7 14 13 119 487Se 3.45 1.62 1 1Br 3.75 3.14 4 5 15

Pb 8.39 5 26 236

Table 8 Concentration of PM 2.5in the ambient air of urban areasaround the world

a Average for the six Braziliancities evaluated b S in sulfate form

Table 9 Annual mean PM 2.5 concentrations, PM 2.5 concentrations inexcess of the air quality guideline a , annual mortality rate, andestimated number of deaths attributed to the excess PM 2.5 concen-trations, by metropolitan region

City Annual meanPM 2.5 ( g m3 )

Excess( g m3 )

Annualmortalityrate (%)

Excessmortality b(thousand)

So Paulo 28.1 18.1 11 9.7Rio de Janeiro 17.2 7.2 4.3 2.7Belo Horizonte 14.7 4.7 2.8 0.5Porto Alegre 13.4 3.4 2.0 0.4Curitiba 14.4 4.4 2.6 0.3

a WHO annual air quality guideline, 10 g m3

b Number of deaths/year



14/15

showed the highest estimated number of deaths associatedwith the excess PM 2.5 . Through the application of themethodology recommended by WHO we found that thehealth impact, measured in terms of the excess mortalityrisk, was that more than 13,000 deaths per year wereattributable to air pollution in the metropolitan regionsunder study.

Conclusions

In this study, we sampled urban atmospheric aerosol PM 2.5for more than 1 year at sites with high traffic volumeswithin six Brazilian state capitals. The concentrations of PM 2.5 and black carbon were found to be lower in thesummer months (November to February). Recife was theonly city in which the profile differed, concentrations thereremaining relatively constant throughout the year. Asexpected, meteorological conditions had a significant influence on pollutant concentrations, accounting for the peak concentrations.

Considering the trace element values obtained throughEDXRF analysis, it was found that a high fraction of elements were derived from mineral dust, anthropogenic particles, S (a combustion product), and the burning of diesel, as well as from industries and residual oil combus-tion. Potassium also appeared in high concentrations, whichis probably attributable to biomass burning in central andnorthern Brazil, including the burning of sugarcane in the process of ethanol production.

In five of the six cities evaluated, the annual mean PM 2.5concentrations exceeded the air quality standard establishedfor this pollutant by European and American legislation.The results of this study show the importance of imple-menting programs aimed at controlling the emissions of fine particulate matter in urban areas.

Acknowledgment This work was supported by the Hewlett Foundation. We are grateful to the following research institutionsfor the sampling infrastructure and technical support: the Depart-ment of Anatomy, Center for Biological Sciences, UniversidadeFederal de Pernambuco (UFPE, Federal University of Pernam- buco); the Respiratory Physiology Laboratory, Carlos Chagas FilhoInstitute of Biophysics, Universidade Federal do Rio de Janeiro

(UFRJ, Federal University of Rio de Janeiro); the PathologyDepartment, Universidade Federal de Minas Gerais (UFMG,Federal University of Minas Gerais) School of Medicine; theChemistry Institute, Universidade Federal do Paran (UFPR,Federal University of Paran); the Department of PhysiologicalSciences, Porto Alegre Federal Foundation for Medical Sciences;and the Instituto Nacional de Meteorologia (INMET, NationalMeteorology Institute).

Open Access This article is distributed under the terms of theCreative Commons Attribution Noncommercial License which per-mits any noncommercial use, distribution, and reproduction in anymedium, provided the original author(s) and source are credited.

References

Akyuz M, abuk H (2009) Meteorological variations of PM 2.5 /PM 10concentrations and particle-associated polycyclic aromatic hydro-carbons in the atmospheric environment of Zonguldak, Turkey. JHazard Mater 170:13 21

Andrade MF, Orsini C, Maenhaut W (1994) Relation between aerosolsources and meteorological parameters for inhalable atmospheric particles in Sao Paulo City, Brazil. Atmos Environ 28:2307 2315

Andrade MF, Miranda RM, Fornaro A, Kerr AS, Andr PA, Saldiva P(2010) Air quality, atmosphere and health. doi: 10.1007/s11869-010-0104-5

Bell ML, Dominici F, Ebisu K, Zeger SL, Samet JM (2007) Spatialand temporal variation in PM 2.5 chemical composition in theUnited States for health effects studies. Environ Health Perspect 115(7):989 995

BEN (2009) Balano Energtico Nacional (Ano-Base 2008), Minis-trio das Minas e Energia. http://www.mme.gov.br/mme/galerias/ arquivos/publicacoes/BEN/5_-_Textos_Analiticos/Textos_Analxticos_2008_x12_a_32x_1.pdf Accessed 11 June 2010

Boures LCS, Orsini CMQ (1981) Fine and coarse elementalcomponents in the urban aerosol of So Paulo, Brazil. NuclInstrum Methods 181:417 424

Braga CF, Teixeira EC, Meira L, Wieganda F, Yoneama ML, Dias JF(2005) Elemental composition of PM 10 and PM 2.5 in urbanenvironment in South Brazil. Atmos Environ 39:1801 1815

Castanho ADA, Artaxo P (2001) Wintertime and summertime SoPaulo aerosol source apportionment study. Atmos Environ35:4889 4902

CETESB (2009) Relatrio de qualidade do ar no Estado de So Paulo,2008 (in Portuguese www.cetesb.sp.gov.br )Accessed 11 June 2010

Clarke AD, Noone KJ, Heintzenberg J, Warren SG, Covert DS (1987)Aerosol light-absorption measurement techniques analysis andintercomparisons. Atmos Environ 21(6):1455 1465

Cohen DD, Crawford J, Stelcer E, Bac VT (2010) Characterisationand source apportionment of fine particulate sources at Hanoifrom 2001 to 2008. Atmos Environ 44:320 328

Dallarosa J, Calesso ET, Meira L, Wiegand F (2008) Study of thechemical elements and polycyclic aromatic hydrocarbons inatmospheric particles of PM 10 and PM 2.5 in the urban and ruralareas of South Brazil. Atmos Res 89:76 92

Datasus, Ministrio da Sade, Informaes de Sade (2008) - http:// tabnet.datasus.gov.br/cgi/deftohtm.exe?sim/cnv/obtbr.def Accessed 16 January 2009

Ferreira da Silva M, Vicente de Assuno J, Andrade MF, PesqueroCR (2010) Characterization of metal and trace element contentsof particulate matter (PM 10 ) emitted by vehicles running onBrazilian fuels hydrated ethanol and gasoline with 22% of anhydrous ethanol. J Toxicol Environ Health A 73(13 14):901 909

Finlayson-Pitts BJ, Pitts J (2000) Chemistry of the upper and lower atmosphere: theory, experiments and applications. Academic

Press, San DiegoFontenele APG, Pedrotti JJ, Fornaro A (2009) Avaliao de metaistraos e ons majoritrios em guas de chuva na cidade de SoPaulo. Quim Nova 32:839 844. doi:10.1590/S0100-40422009000400003

Fornaro A, Gutz IGR (2006) Wet deposition and related atmosphericchemistry in the So Paulo metropolis, Brazil: Part 3. Trends in precipitation chemistry during 1983 2003 period. Atmos Environ40:5893 5901

Geller MD, Ntziachristos L, Mamakos A, Samaras Z, Schmitz DA,Froines JR, Sioutas C (2006) Physicochemical and redoxcharacteristics of particulate matter (PM) emitted from gasolineand diesel passenger cars. Atmos Environ 40:6988 7004

http://dx.doi.org/10.1007/s11869-010-0104-5http://dx.doi.org/10.1007/s11869-010-0104-5http://www.mme.gov.br/mme/galerias/arquivos/publicacoes/BEN/5_-_Textos_Analiticos/Textos_Analxticos_2008_x12_a_32x_1.pdfhttp://www.mme.gov.br/mme/galerias/arquivos/publicacoes/BEN/5_-_Textos_Analiticos/Textos_Analxticos_2008_x12_a_32x_1.pdfhttp://www.mme.gov.br/mme/galerias/arquivos/publicacoes/BEN/5_-_Textos_Analiticos/Textos_Analxticos_2008_x12_a_32x_1.pdfhttp://www.cetesb.sp.gov.br/http://tabnet.datasus.gov.br/cgi/deftohtm.exe?sim/cnv/obtbr.defhttp://tabnet.datasus.gov.br/cgi/deftohtm.exe?sim/cnv/obtbr.defhttp://dx.doi.org/10.1590/S0100-40422009000400003http://dx.doi.org/10.1590/S0100-40422009000400003http://dx.doi.org/10.1590/S0100-40422009000400003http://dx.doi.org/10.1590/S0100-40422009000400003http://tabnet.datasus.gov.br/cgi/deftohtm.exe?sim/cnv/obtbr.defhttp://tabnet.datasus.gov.br/cgi/deftohtm.exe?sim/cnv/obtbr.defhttp://www.cetesb.sp.gov.br/http://www.mme.gov.br/mme/galerias/arquivos/publicacoes/BEN/5_-_Textos_Analiticos/Textos_Analxticos_2008_x12_a_32x_1.pdfhttp://www.mme.gov.br/mme/galerias/arquivos/publicacoes/BEN/5_-_Textos_Analiticos/Textos_Analxticos_2008_x12_a_32x_1.pdfhttp://www.mme.gov.br/mme/galerias/arquivos/publicacoes/BEN/5_-_Textos_Analiticos/Textos_Analxticos_2008_x12_a_32x_1.pdfhttp://dx.doi.org/10.1007/s11869-010-0104-5http://dx.doi.org/10.1007/s11869-010-0104-5


15/15

Godoy MLDP, Godoy JM, Roldo LA, Soluri DS, Donagemma RA(2009) Coarse and fine aerosol source apportionment in Rio deJaneiro, Brazil. Atmos Environ 43:2366 2374

Heintzenberg J (1982) Size segregated measurements of particulateelemental carbon and aerosol light-absorption at remote Arcticlocations. Atmos Environ 16:2461 2469

IBGE Instituto Brasileiro de Geografia e Estatstica ( http://www.ibge.gov.br/english/ ) (2009 and 2008) http://www.ibge.gov.br/ cidadesat/topwindow.htm?1 Accessed 8 October 2010

Khan MdF, Shirasuna Y, Hirano K, Masunaga S (2010) Characteriza-tion of PM 2.5 , PM2.5 10 and PM >10 in ambient air, Yokohama,Japan. Atmos Res 96:159 172

Kulshrestha A, Satsangi PG, Masih J, Taneja A (2009) Metalconcentration of PM 2.5 and PM 10 particles and seasonalvariations in urban and rural environment of Agra, India. SciTotal Environ 407:6196 6204

Kundu S, Kawamura K, Andreae TW, Hofferd A, Andreae MO (2010)Diurnal variation in the water-soluble inorganic ions, organic carbonand isotopic compositions of total carbon and nitrogen in biomass burning aerosols from the LBA-SMOCC campaign in Rondonia,Brazil. J Aero Sci 41(1):118 133. doi: 10.1016/j.jaerosci.2009.08.006

Lanki T, Pekkanen J, Aalto P, ElosuaR, Berglind N, D'Ippoliti D, Kulmala M, Nyberg F, Peters A, Picciotto S, Salomaa V, Sunyer J, TiittanenP, von Klot S, Forastiere F (2006) Associations of traffic related air pollutants with hospitalization for first acute myocardial infarction:the HEAPSS study. Occup Environ Med 63(12):844 851

Liu W, WangY, RusselA, Edgerton ES (2005) Atmosphericaerosol over two urban rural pairs in the southeastern United States: chemicalcomposition and possible sources. Atmos Environ 39:4453 4470

Miranda RM, Andrade MF (2004) Physicochemical characteristics of atmospheric aerosol during winter in the So Paulo MetropolitanArea in Brazil. Atmos Environ 39(33):6188 6193

Miranda RM, Tomaz E (2008) Characterization of urban aerosol inCampinas, So Paulo, Brazil. Atmos Res 87:147 157

Miranda RM, Andrade MF, Worobiec A, Van Grieken R (2002)Characterization of aerosol particles in the So Paulo MetropolitanArea. Atmos Environ 36(2):345 352

Orsini CQ, Tabacniks M, Artaxo P, Andrade MF, Kerr AS (1986)Characteristics of fine and coarse particles of natural and urbanaerosols of Brazil. Atmos Environ 20:2259 2269

Ostro B (2006) Health impact assessment. In: air quality Guidelines Global Update 2005. World Health Organization, Copenhagen, pp 153 171

Pope CA, Dockery DW (2006) Health effects of fine particulate air pollut ion: lines that connect. J Air Waste Manage Assoc50:1308 1320

Rodriguez CAM, Rocha RP, Bombardi R (2010) On the development of summer thunderstorms in the city of So Paulo: mean meteorologicalcharacteristics and pollution effect. Atmos Res 96:477 488

Ruellan S, Cachier H (2001) Characterization of fresh particulatevehicular exhausts near a Paris high flow road. Atmos Environ35:453 468

Saldiva PH, Lichtenfels AJ, Paiva PS, Barone IA, Martins MA,Massad E, Pereira JC, Xavier VP, Singer JM, Bohm GM (1994)Association between air pollution and mortality due to respira-tory diseases in children in So Paulo, Brazil: a preliminaryreport. Environ Res 65(2):218 225

Sanchez-Ccoyllo OR, Ynoue RY, Martins LD, Astolfo R, Miranda RM, Freitas ED, Borges AS, Fornaro A, Freitas H, Moreira A,Andrade MF (2009) Vehicular particulate matter emissions inroads tunnels in So Paulo, Brazil. Environ Monit Assess149:241 249. doi: 10.1007/s10661-008-0198-5

Seinfeld JH, Pandis SN (1998) Atmospheric chemistry and physics:from air pollution to climate change. Wiley, New York

Sillanpaa M, Hillamo R, Kerminen VM, Pakknen T, Salonen R,Pennanen A, Aarnio P, Koskentalo T (2000) Chemical compo-sition and mass balance of an urban aerosol during variousseasons. J Aerosol Sci 31(suppl 1):S309 S310

Sillanpaa M, Hillamoa R, Saarikoskia S, Freya A, Pennanenb A,Makkonena U, Spolnikc Z, Van Grieken R, Branis M, BrunekreefeB, Chalbotg MC, Kuhlbuschh T, Sunyeri J, Kerminenj VM,Kulmalak M, Salonen RO (2006) Chemical composition and massclosure of particulate matter at six urban sites in Europe. AtmosEnviron 40:S212 S223

Stolzel M, Breitner S, Cyrys J, Pitz M, Wlke G, Kreyling W,Heinrich J, Wichmann HE, Peters A (2007) Daily mortality and particulate matter in different size classes in Erfurt, Germany. JExpo Sci Environ Epidemiol 17(5):458 467

Tai APK, Mickley LJ, Jacob D (2010) Correlations between fine particulate matter (PM 2.5 ) and meteorological variables in theUnited States: implications for the sensitivity of PM 2.5 to climatechange. Atmos Environ 44:3976 3984

Viidanojaa J, Sillanpaa M, Jaakko Laakia J, Kerminena VM, HillamoR, Aarnio P, Koskentalo T (2002) Organic and black carbon inPM 2.5 and PM 10 : 1 year of datafrom an urban site in Helsinki,Finland. Atmos Environ 36:3183 3193

WHO World Health Organization (2006) Air quality guidelines,global update 2005: particulate matter, ozone, nitrogen dioxideand sulphur dioxide. Germany; chap. 10:217 305

Zhao X, Zhang X, Xu X, Xu J, Meng W, Pu W (2009) Seasonal anddiurnal variations of ambient PM 2.5 concentration in urban andrural environments in Beijing. Atmos Environ 43:2893 2900

http://www.ibge.gov.br/english/http://www.ibge.gov.br/english/http://www.ibge.gov.br/cidadesat/topwindow.htm?1http://www.ibge.gov.br/cidadesat/topwindow.htm?1http://dx.doi.org/10.1016/j.jaerosci.2009.08.006http://dx.doi.org/10.1007/s10661-008-0198-5http://dx.doi.org/10.1007/s10661-008-0198-5http://dx.doi.org/10.1016/j.jaerosci.2009.08.006http://www.ibge.gov.br/cidadesat/topwindow.htm?1http://www.ibge.gov.br/cidadesat/topwindow.htm?1http://www.ibge.gov.br/english/http://www.ibge.gov.br/english/

A Representative Survey of PM2.5 Mass

Documents

Transcript of A Representative Survey of PM2.5 Mass