Enhancing spatial inference of air pollution using machine learning techniques with low-cost monitors in data-limited scenarios

Ensuring environmental justice necessitates equitable access to air quality data, particularly for vulnerable communities. However, traditional air quality data from reference monitors can be costly and challenging to interpret without in-depth knowledge of local meteorology. Low-cost monitors present an opportunity to enhance data availability in developing countries and enable the establishment of local monitoring networks. While machine learning models have shown promise in atmospheric dispersion modelling, many existing approaches rely on complementary data sources that are inaccessible in low-income areas, such as smartphone tracking and real-time traffic monitoring. This study addresses these limitations by introducing deep learning-based models for particulate matter dispersion at the neighbourhood scale. The models utilize data from low-cost monitors and widely available free datasets, delivering root mean square errors (RMSE) below 2.9 μg m-3 for PM1, PM2.5, and PM10. The sensitivity analysis shows that the most important inputs to the models were the nearby monitors' PM concentrations, boundary layer dissipation and height, and precipitation variables. The models presented different sensitivities to each road type, and an RMSE below the regional differences, evidencing the learning of the spatial dependencies. This breakthrough paves the way for applications in various vulnerable localities, significantly improving air pollution data accessibility and contributing to environmental justice. Moreover, this work sets the stage for future research endeavours in refining the models and expanding data accessibility using alternative sources.

Urban heat mitigation by green and blue infrastructure: Drivers, effectiveness, and future needs

The combination of urbanization and global warming leads to urban overheating and compounds the frequency and intensity of extreme heat events due to climate change. Yet, the risk of urban overheating can be mitigated by urban green-blue-grey infrastructure (GBGI), such as parks, wetlands, and engineered greening, which have the potential to effectively reduce summer air temperatures. Despite many reviews, the evidence bases on quantified GBGI cooling benefits remains partial and the practical recommendations for implementation are unclear. This systematic literature review synthesizes the evidence base for heat mitigation and related co-benefits, identifies knowledge gaps, and proposes recommendations for their implementation to maximize their benefits. After screening 27,486 papers, 202 were reviewed, based on 51 GBGI types categorized under 10 main divisions. Certain GBGI (green walls, parks, street trees) have been well researched for their urban cooling capabilities. However, several other GBGI have received negligible (zoological garden, golf course, estuary) or minimal (private garden, allotment) attention. The most efficient air cooling was observed in botanical gardens (5.0 ± 3.5°C), wetlands (4.9 ± 3.2°C), green walls (4.1 ± 4.2°C), street trees (3.8 ± 3.1°C), and vegetated balconies (3.8 ± 2.7°C). Under changing climate conditions (2070-2100) with consideration of RCP8.5, there is a shift in climate subtypes, either within the same climate zone (e.g., Dfa to Dfb and Cfb to Cfa) or across other climate zones (e.g., Dfb [continental warm-summer humid] to BSk [dry, cold semi-arid] and Cwa [temperate] to Am [tropical]). These shifts may result in lower efficiency for the current GBGI in the future. Given the importance of multiple services, it is crucial to balance their functionality, cooling performance, and other related co-benefits when planning for the future GBGI. This global GBGI heat mitigation inventory can assist policymakers and urban planners in prioritizing effective interventions to reduce the risk of urban overheating, filling research gaps, and promoting community resilience.

Emission factors for a biofuel impacted fleet in south America's largest metropolitan area

The Metropolitan Area of São Paulo (MASP) is among the largest urban areas in the Southern Hemisphere. Vehicular emissions are of great concern in metropolitan areas and MASP is unique due to the use of biofuels on a large scale (sugar-cane ethanol and biodiesel). In this work, tunnel measurements were employed to assess vehicle emissions and to calculate emission factors (EFs) for heavy-duty and light-duty vehicles (HDVs and LDVs). The EFs were determined for particulate matter (PM) and its chemical compounds. The EFs obtained for 2018 were compared with previous tunnel experiments performed in the same area. An overall trend of reduction of fine and coarse PM, organic carbon (OC), and elemental carbon (EC) EFs for both LDVs and HDVs was observed if compared to those observed in past years, suggesting the effectiveness of vehicular emissions control policies implemented in Brazil. A predominance of Fe, Cu, Al, and Ba metals emission was observed for the LDV fleet in the fine fraction. Cu presented higher emissions than two decades ago, which was associated with the increased use of ethanol fuel in the region. For HDVs, Zn and Pb were mostly emitted in the fine mode and were linked with lubricating oil emissions from diesel vehicles. A predominance in the emission of three- and four-ring polycyclic aromatic hydrocarbons (PAHs) for HDVs and five-ring PAHs for LDVs agreed with what was observed in previous studies. The use of biofuels may explain the lower PAH emissions for LDVs (including carcinogenic BaP) compared to those observed in other countries. The tendency observed was that LDVs emit higher amounts of carcinogenic species. The use of these real EFs in air quality modeling resulted in more accurate simulations of PM concentrations, showing the importance of updating data with real-world measurements.

Health Risk Assessment of Exposure to Air Pollutants Exceeding the New WHO Air Quality Guidelines (AQGs) in São Paulo, Brazil

We applied the AirQ+ model to analyze the 2021 data within our study period (15 December 2020 to 17 June 2022) to quantitatively estimate the number of specific health outcomes from long- and short-term exposure to atmospheric pollutants that could be avoided by adopting the new World Health Organization Air Quality Guidelines (WHO AQGs) in São Paulo, Southeastern Brazil. Based on temporal variations, PM_2.5, PM₁₀, NO₂, and O₃ exceeded the 2021 WHO AQGs on up to 54.4% of the days during sampling, mainly in wintertime (June to September 2021). Reducing PM_2.5 values in São Paulo, as recommended by the WHO, could prevent 113 and 24 deaths from lung cancer (LC) and chronic obstructive pulmonary disease (COPD) annually, respectively. Moreover, it could avoid 258 and 163 hospitalizations caused by respiratory (RD) and cardiovascular diseases (CVD) due to PM_2.5 exposure. The results for excess deaths by RD and CVD due to O₃ were 443 and 228, respectively, and 90 RD hospitalizations due to NO₂. Therefore, AirQ+ is a useful tool that enables further elaboration and implementation of air pollution control strategies to reduce and prevent hospital admissions, mortality, and economic costs due to exposure to PM_2.5, O₃, and NO₂ in São Paulo.

Particulate matter fingerprints in biofuel impacted tunnels in South America's largest metropolitan area

This study characterized the chemical composition of particulate matter (PM) from light- (LDV) and heavy-duty (HDV) vehicles based on two traffic tunnel samplings carried out in the megacity of São Paulo (Brazil), which has >7 million vehicles and intense biofuel use. The samples were collected with high-volume samplers and analyzed using chemical characterization techniques (ion and gas chromatography, thermal-optical analysis, and inductively coupled plasma mass spectroscopy). Chemical source profiles (%) were calculated based on the measurements performed inside and outside the tunnels. Identifying a high abundance of Fe and Cu for traffic-related PM in the LDV-impacted tunnel was possible, linked with the emission of vehicles powered by ethanol and gasohol (gasoline and ethanol blend). We calculated diagnostic ratios (e.g., EC/Cu, Fe/Cu, pyrene/benzo[a]pyrene, pyrene/benzo[b]fluoranthene, and fluoranthene/benzo[b]fluoranthene) characteristic of fuel exhausts (diesel/biodiesel and ethanol/gasohol), allowing their use in the assessment of the temporal variation of the fuel type used in urban sites. Element diagnostic ratios (Cu/Sb and Fe/Cu) pointed to the predominance of LDVs exhaust-related copper and can differentiate LDVs exhaust from brake wear emissions. The carbonaceous fraction EC3 was suggested as an HDV emission tracer. A higher total polycyclic aromatic hydrocarbons (PAHs) fraction of traffic-related PM_2.5 was observed in the HDV-impacted tunnel, with a predominance of diesel-related pyrene and fluoranthene, as well as higher oxy-PAHs (e.g., 9,10-anthraquinone, associated with biodiesel blends) abundances. However, carcinogenic species presented higher abundances for the LDV-impacted tunnel (e.g., benzo[a]pyrene). These findings highlighted the impact of biofuels on the characteristic ratios of chemical species and pointed to possible markers for LDVs and HDVs exhausts.

In-kitchen aerosol exposure in twelve cities across the globe

Poor ventilation and polluting cooking fuels in low-income homes cause high exposure, yet relevant global studies are limited. We assessed exposure to in-kitchen particulate matter (PM_2.5 and PM₁₀) employing similar instrumentation in 60 low-income homes across 12 cities: Dhaka (Bangladesh); Chennai (India); Nanjing (China); Medellín (Colombia); São Paulo (Brazil); Cairo (Egypt); Sulaymaniyah (Iraq); Addis Ababa (Ethiopia); Akure (Nigeria); Blantyre (Malawi); Dar-es-Salaam (Tanzania) and Nairobi (Kenya). Exposure profiles of kitchen occupants showed that fuel, kitchen volume, cooking type and ventilation were the most prominent factors affecting in-kitchen exposure. Different cuisines resulted in varying cooking durations and disproportional exposures. Occupants in Dhaka, Nanjing, Dar-es-Salaam and Nairobi spent > 40% of their cooking time frying (the highest particle emitting cooking activity) compared with ∼ 68% of time spent boiling/stewing in Cairo, Sulaymaniyah and Akure. The highest average PM_2.5 (PM₁₀) concentrations were in Dhaka 185 ± 48 (220 ± 58) μg m^-3 owing to small kitchen volume, extensive frying and prolonged cooking compared with the lowest in Medellín 10 ± 3 (14 ± 2) μg m^-3. Dual ventilation (mechanical and natural) in Chennai, Cairo and Sulaymaniyah reduced average in-kitchen PM_2.5 and PM₁₀ by 2.3- and 1.8-times compared with natural ventilation (open doors) in Addis Ababa, Dar-es-Salam and Nairobi. Using charcoal during cooking (Addis Ababa, Blantyre and Nairobi) increased PM_2.5 levels by 1.3- and 3.1-times compared with using natural gas (Nanjing, Medellin and Cairo) and LPG (Chennai, Sao Paulo and Sulaymaniyah), respectively. Smaller-volume kitchens (<15 m³; Dhaka and Nanjing) increased cooking exposure compared with their larger-volume counterparts (Medellin, Cairo and Sulaymaniyah). Potential exposure doses were highest for Asian, followed by African, Middle-eastern and South American homes. We recommend increased cooking exhaust extraction, cleaner fuels, awareness on improved cooking practices and minimising passive occupancy in kitchens to mitigate harmful cooking emissions.

A global observational analysis to understand changes in air quality during exceptionally low anthropogenic emission conditions

This global study, which has been coordinated by the World Meteorological Organization Global Atmospheric Watch (WMO/GAW) programme, aims to understand the behaviour of key air pollutant species during the COVID-19 pandemic period of exceptionally low emissions across the globe. We investigated the effects of the differences in both emissions and regional and local meteorology in 2020 compared with the period 2015-2019. By adopting a globally consistent approach, this comprehensive observational analysis focuses on changes in air quality in and around cities across the globe for the following air pollutants PM_2.5, PM₁₀, PMC (coarse fraction of PM), NO₂, SO₂, NOx, CO, O₃ and the total gaseous oxidant (OX = NO₂ + O₃) during the pre-lockdown, partial lockdown, full lockdown and two relaxation periods spanning from January to September 2020. The analysis is based on in situ ground-based air quality observations at over 540 traffic, background and rural stations, from 63 cities and covering 25 countries over seven geographical regions of the world. Anomalies in the air pollutant concentrations (increases or decreases during 2020 periods compared to equivalent 2015-2019 periods) were calculated and the possible effects of meteorological conditions were analysed by computing anomalies from ERA5 reanalyses and local observations for these periods. We observed a positive correlation between the reductions in NO₂ and NOx concentrations and peoples' mobility for most cities. A correlation between PMC and mobility changes was also seen for some Asian and South American cities. A clear signal was not observed for other pollutants, suggesting that sources besides vehicular emissions also substantially contributed to the change in air quality. As a global and regional overview of the changes in ambient concentrations of key air quality species, we observed decreases of up to about 70% in mean NO₂ and between 30% and 40% in mean PM_2.5 concentrations over 2020 full lockdown compared to the same period in 2015-2019. However, PM_2.5 exhibited complex signals, even within the same region, with increases in some Spanish cities, attributed mainly to the long-range transport of African dust and/or biomass burning (corroborated with the analysis of NO₂/CO ratio). Some Chinese cities showed similar increases in PM_2.5 during the lockdown periods, but in this case, it was likely due to secondary PM formation. Changes in O₃ concentrations were highly heterogeneous, with no overall change or small increases (as in the case of Europe), and positive anomalies of 25% and 30% in East Asia and South America, respectively, with Colombia showing the largest positive anomaly of ~70%. The SO₂ anomalies were negative for 2020 compared to 2015-2019 (between ~25 to 60%) for all regions. For CO, negative anomalies were observed for all regions with the largest decrease for South America of up to ~40%. The NO₂/CO ratio indicated that specific sites (such as those in Spanish cities) were affected by biomass burning plumes, which outweighed the NO₂ decrease due to the general reduction in mobility (ratio of ~60%). Analysis of the total oxidant (OX = NO₂ + O₃) showed that primary NO₂ emissions at urban locations were greater than the O₃ production, whereas at background sites, OX was mostly driven by the regional contributions rather than local NO₂ and O₃ concentrations. The present study clearly highlights the importance of meteorology and episodic contributions (e.g., from dust, domestic, agricultural biomass burning and crop fertilizing) when analysing air quality in and around cities even during large emissions reductions. There is still the need to better understand how the chemical responses of secondary pollutants to emission change under complex meteorological conditions, along with climate change and socio-economic drivers may affect future air quality. The implications for regional and global policies are also significant, as our study clearly indicates that PM_2.5 concentrations would not likely meet the World Health Organization guidelines in many parts of the world, despite the drastic reductions in mobility. Consequently, revisions of air quality regulation (e.g., the Gothenburg Protocol) with more ambitious targets that are specific to the different regions of the world may well be required.

The state of science on severe air pollution episodes: Quantitative and qualitative analysis

Severe episodic air pollution blankets entire cities and regions and have a profound impact on humans and their activities. We compiled daily fine particle (PM_2.5) data from 100 cities in five continents, investigated the trends of number, frequency, and duration of pollution episodes, and compared these with the baseline trend in air pollution. We showed that the factors contributing to these events are complex; however, long-term measures to abate emissions from all anthropogenic sources at all times is also the most efficient way to reduce the occurrence of severe air pollution events. In the short term, accurate forecasting systems of such events based on the meteorological conditions favouring their occurrence, together with effective emergency mitigation of anthropogenic sources, may lessen their magnitude and/or duration. However, there is no clear way of preventing events caused by natural sources affected by climate change, such as wildfires and desert dust outbreaks.

Potential health risks due to in-car aerosol exposure across ten global cities

Car microenvironments significantly contribute to the daily pollution exposure of commuters, yet health and socioeconomic studies focused on in-car exposure are rare. This study aims to assess the relationship between air pollution levels and socioeconomic indicators (fuel prices, city-specific GDP, road density, the value of statistical life (VSL), health burden and economic losses resulting from exposure to fine particulate matter ≤2.5 µm; PM_2.5) during car journeys in ten cities: Dhaka (Bangladesh); Chennai (India); Guangzhou (China); Medellín (Colombia); São Paulo (Brazil); Cairo (Egypt); Sulaymaniyah (Iraq); Addis Ababa (Ethiopia); Blantyre (Malawi); and Dar-es-Salaam (Tanzania). Data collected by portable laser particle counters were used to develop a proxy of car-user exposure profiles. Hotspots on all city routes displayed higher PM_2.5 concentrations and disproportionately high inhaled doses. For instance, the time spent at the hotspots in Guangzhou and Addis Ababa was 26% and 28% of total trip time, but corresponded to 54% and 56%, respectively, of the total PM_2.5 inhaled dose. With the exception of Guangzhou, all the cities showed a decrease in per cent length of hotspots with an increase in GDP and VSL. Exposure levels were independent of fuel prices in most cities. The largest health burden related to in-car PM_2.5 exposure was estimated for Dar-es-Salam (81.6 ± 39.3 μg m^-3), Blantyre (82.9 ± 44.0) and Dhaka (62.3 ± 32.0) with deaths per 100,000 of the car commuting population per year of 2.46 (2.28-2.63), 1.11 (0.97-1.26) and 1.10 (1.05-1.15), respectively. However, the modest health burden of 0.07 (0.06-0.08), 0.10 (0.09-0.12) and 0.02 (0.02-0.03) deaths per 100,000 of the car commuting population per year were estimated for Medellin (23 ± 13.7 μg m^-3), São Paulo (25.6 ± 11.7) and Sulaymaniyah (22.4 ± 15.0), respectively. Lower GDP was found to be associated with higher economic losses due to health burdens caused by air pollution in most cities, indicating a socioeconomic discrepancy. This assessment of health and socioeconomic parameters associated with in-car PM_2.5 exposure highlights the importance of implementing plausible solutions to make a positive impact on peoples' lives in these cities.

Evolution of Vehicle Emission Factors in a Megacity Affected by Extensive Biofuel Use: Results of Tunnel Measurements in São Paulo, Brazil

Since 2001, four emission measurement campaigns have been conducted in multiple traffic tunnels in the megacity of São Paulo, Brazil, an area with a fleet of more than 7 million vehicles running on fuels with high biofuel contents: gasoline + ethanol for light-duty vehicles (LDVs) and diesel + biodiesel for heavy-duty vehicles (HDVs). Emission factors for LDVs and HDVs were calculated using a carbon balance method, the pollutants considered including nitrogen oxides (NO_x), carbon monoxide (CO), and sulfur dioxide, as well as carbon dioxide and ethanol. From 2001 to 2018, fleet-average emission factors for LDVs and HDVs, respectively, were found to decrease by 4.9 and 5.1% per year for CO and by 5.5 and 4.2% per year for NO_x. These reductions demonstrate that regulations for vehicle emissions adopted in Brazil in the last 30 years improved air quality in the megacity of São Paulo significantly, albeit with a clear delay. These findings, especially those for CO, indicate that official emission inventories underestimate vehicle emissions. Here, we demonstrated that the adoption of emission factors calculated under real-world conditions can dramatically improve air quality modeling in the region.

In-car particulate matter exposure across ten global cities

Cars are a commuting lifeline worldwide, despite contributing significantly to air pollution. This is the first global assessment on air pollution exposure in cars across ten cities: Dhaka (Bangladesh); Chennai (India); Guangzhou (China); Medellín (Colombia); São Paulo (Brazil); Cairo (Egypt); Sulaymaniyah (Iraq); Addis Ababa (Ethiopia); Blantyre (Malawi); and Dar-es-Salaam (Tanzania). Portable laser particle counters were used to develop a proxy of car-user exposure profiles and analyse the factors affecting particulate matter ≤2.5 μm (PM_2.5; fine fraction) and ≤10 μm (PM_2.5-10; coarse fraction). Measurements were carried out during morning, off- and evening-peak hours under windows-open and windows-closed (fan-on and recirculation) conditions on predefined routes. For all cities, PM_2.5 and PM₁₀ concentrations were highest during windows-open, followed by fan-on and recirculation. Compared with recirculation, PM_2.5 and PM₁₀ were higher by up to 589% (Blantyre) and 1020% (São Paulo), during windows-open and higher by up to 385% (São Paulo) and 390% (São Paulo) during fan-on, respectively. Coarse particles dominated the PM fraction during windows-open while fine particles dominated during fan-on and recirculation, indicating filter effectiveness in removing coarse particles and a need for filters that limit the ingress of fine particles. Spatial variation analysis during windows-open showed that pollution hotspots make up to a third of the total route-length. PM_2.5 exposure for windows-open during off-peak hours was 91% and 40% less than morning and evening peak hours, respectively. Across cities, determinants of relatively high personal exposure doses included lower car speeds, temporally longer journeys, and higher in-car concentrations. It was also concluded that car-users in the least affluent cities experienced disproportionately higher in-car PM_2.5 exposures. Cities were classified into three groups according to low, intermediate and high levels of PM exposure to car commuters, allowing to draw similarities and highlight best practices.

Characterization of particles emitted by pizzerias burning wood and briquettes: a case study at Sao Paulo, Brazil

The burning of biomass in pizza ovens can be an important source of air pollution. Fine particulate matter represents one of the most aggressive pollutants to human health, besides the potential to interfere with global radiative balance. A study in real-world condition was performed in three pizzerias in São Paulo city. Two of the pizzerias used eucalyptus timber logs and one used wooden briquettes. The results from the three pizzerias revealed high average concentrations of PM_2.5: 6171.2 μg/m³ at the exit of the chimney and 68.2 μg/m³ in indoor areas. The burning of briquette revealed lower concentrations of PM_2.5. BC represented approximately 20% and 30% of the PM_2.5 mass concentration in indoor and at chimney exhaust, respectively. Among the trace elements, potassium, chlorine and sulphur were the most prevalent in terms of concentration. Scanning electron microscopy (SEM) analysis revealed particles with an individual and spherical morphology, i.e. the conglomeration of spherical particles, flattened particles in the formation of fibres, the overlapping of layers and the clustering of particles with sponge-like qualities. The average emission factors for PM_2.5 and BC due to the burning of logs were 0.38 g/kg and 0.23 g/kg, respectively. The total emissions of PM_2.5 and BC were 116.73 t/year and 70.65 t/year, respectively, in the burning of timber logs.

Public health implications of particulate matter inside bus terminals in Sao Paulo, Brazil

Good quality ambient air is recognised as an important factor of social justice. In addition, providing access to high-capacity public transportation in big cities is known to be a good practice of social equity, as well as economic and environmental sustainability. However, the health risks associated with air pollution are not distributed equally across cities; the most vulnerable people are more exposed to ambient air as they commute to work and wait for buses or trains at the stations. The overall goal of this work was to assess the determinants of human exposure to particulate matter (PM) during commuting time spent inside bus terminals in the Metropolitan Area of Sao Paulo (MASP), in Brazil. Fine and coarse particles were collected at four bus terminals in the MASP. The concentrations of PM and its harmful constituents (black carbon and metals) were used in order to estimate potential doses and the associated health risk during the time spent at bus terminals in the MASP. Our findings show that bus commuters travelling through the bus terminal in the MASP on weekdays inhaled up to 94% higher doses of PM₁₀ than did those travelling outside the terminal; even on weekends, that difference was as high as 88%. Our risk assessment indicated that time spent inside a bus terminal can result in an intolerable health risk for commuters, mainly because of the Cr present in fine particles. Although bus commuters are exposed to fine particle concentrations up to 2 times lower than the worldwide average, we can affirm that inhalable particles in the MASP bus terminals pose a high carcinogenic risk to the daily users of those terminals, mainly those in the most susceptible groups, which include people with heart or lung disease, older adults and children.

A two decades study on ozone variability and trend over the main urban areas of the São Paulo state, Brazil

In this paper, we analyze the variability of the ozone concentration over São Paulo Macrometropolis, as well the factors, which determined the tendency observed in the last two decades. Time series of hourly ozone concentrations measured at 16 automated stations from an air quality network from 1996 to 2017 were analyzed. The temporal variability of ozone concentrations exhibits well-defined daily and seasonal patterns. Ozone presents a significant positive correlation between the number of cases (thresholds of 100-160 μg m^-3) and the fuel sales of gasohol and diesel. The ozone concentrations do not exhibit significant long-term trends, but some sites present positive trends that occurs in sites in the proximity of busy roads and negative trends that occurs in sites located in residential areas or next to trees. The effect of atmospheric process of transport and ozone formation was analyzed using a quantile regression model (QRM). This statistical model can deal with the nonlinearities that appear in the relationship of ozone and other variables and is applicable to time series with non-normal distribution. The resulting model explains 0.76% of the ozone concentration variability (with global coefficient of determination R¹ = 0.76) providing a better representation than an ordinary least square regression model (with coefficient of determination R² = 0.52); the effect of radiation and temperature are the most critical in determining the highest ozone quantiles.

Assessing the impact of PM2.5 on respiratory disease using artificial neural networks

Understanding the impact on human health during peak episodes in air pollution is invaluable for policymakers. Particles less than PM_2.5 can penetrate the respiratory system, causing cardiopulmonary and other systemic diseases. Statistical regression models are usually used to assess air pollution impacts on human health. However, when there are databases missing, linear statistical regression may not process well and alternative data processing should be considered. Nonlinear Artificial Neural Networks (ANN) are not employed to research environmental health pollution even though another advantage in using ANN is that the output data can be expressed as the number of hospital admissions. This research applied ANN to assess the impact of air pollution on human health. Three well-known ANN were tested: Multilayer Perceptron (MLP), Extreme Learning Machines (ELM) and Echo State Networks (ESN), to assess the influence of PM_2.5, temperature, and relative humidity on hospital admissions due to respiratory diseases. Daily PM_2.5 levels were monitored, and hospital admissions for respiratory illness were obtained, from the Brazilian hospital information system for all ages during two sampling campaigns (2008-2011 and 2014-2015) in Curitiba, Brazil. During these periods, the daily number of hospital admissions ranged from 2 to 55, PM_2.5 concentrations varied from 0.98 to 54.2 μg m^-3, temperature ranged from 8 to 26 °C, and relative humidity ranged from 45 to 100%. Of the ANN used in this study, MLP gave the best results showing a significant influence of PM_2.5, temperature and humidity on hospital attendance after one day of exposure. The Anova Friedman's test showed statistical difference between the appliance of each ANN model (p < .001) for 1 lag day between PM_2.5 exposure and hospital admission. ANN could be a more sensitive method than statistical regression models for assessing the effects of air pollution on respiratory health, and especially useful when there is limited data available.

The relationship between aerosol particles chemical composition and optical properties to identify the biomass burning contribution to fine particles concentration: a case study for São Paulo city, Brazil

The air quality in the Metropolitan Area of São Paulo (MASP) is primarily determined by the local pollution source contribution, mainly the vehicular fleet, but there is a concern about the role of remote sources to the fine mode particles (PM_2.5) concentration and composition. One of the most important remote sources of atmospheric aerosol is the biomass burning emissions from São Paulo state's inland and from the central and north portions of Brazil. This study presents a synergy of different measurements of atmospheric aerosol chemistry and optical properties in the MASP in order to show how they can be used as a tool to identify particles from local and remote sources. For the clear identification of the local and remote source contribution, aerosol properties measurements at surface level were combined with vertical profiles information. Over 15 days in the austral winter of 2012, particulate matter (PM) was collected using a cascade impactor and a Partisol sampler in São Paulo City. Mass concentrations were determined by gravimetry, black carbon concentrations by reflectance, and trace element concentrations by X-ray fluorescence. Aerosol optical properties were studied using a multifilter rotating shadowband radiometer (MFRSR), a Lidar system and satellite data. Optical properties, concentrations, size distributions, and elemental composition of atmospheric particles were strongly related and varied according to meteorological conditions. During the sampling period, PM mean mass concentrations were 17.4 ± 10.1 and 15.3 ± 6.9 μg/m³ for the fine and coarse fractions, respectively. The mean aerosol optical depths at 415 nm and Ångström exponent (AE) over the whole period were 0.29 ± 0.14 and 1.35 ± 0.11, respectively. Lidar ratios reached values of 75 sr. The analyses of the impacts of an event of biomass burning smoke transport to the São Paulo city revealed significant changing on local aerosol concentrations and optical parameters. The identification of the source contributions, local and remote, to the fine particles in MASP can be more precisely achieved when particle size composition and distribution, vertical profile of aerosols, and air mass trajectories are analyzed in combination.

Analysis of atmospheric aerosol (PM2.5) in Recife city, Brazil

Several studies indicate that mortality and morbidity can be well correlated to atmospheric aerosol concentrations with aerodynamic diameter less than 2.5 microm (PM2.5). In this work the PM2.5 at Recife city was analyzed as part of a main research project (INAIRA) to evaluate the air pollution impact on human health in six Brazilian metropolitan areas. The average concentration, for 309 samples (24-hr), from June 2007 to July 2008, was 7.3 microg/m3, with an average of 1.1 microg/m3 of black carbon. The elemental concentrations of samples were obtained by x-ray fluorescence. The concentrations were then used for characterizing the aerosol, and also were employed for receptor modelling to identify the major local sources of PM2.5. Positive matrix factorization analysis indicated six main factors, with four being associated to soil dust, vehicles and sea spray, metallurgical activities, and biomass burning, while for a chlorine factor, and others related to S, Ca, Br, and Na, we could make no specific source association. Principal component analysis also indicated six dominant factors, with some specific characteristics. Four factors were associated to soil dust, vehicles, biomass burning, and sea spray, while for the two others, a chlorine- and copper-related factor and a nickel-related factor, it was not possible to do a specific source association. The association of the factors to the likely sources was possible thanks to meteorological analysis and sources information. Each model, although giving similar results, showed factors' peculiarities, especially for source apportionment. The observed PM2.5 concentration levels were acceptable, notwithstanding the high urbanization of the metropolitan area, probably due to favorable conditions for air pollution dispersion. More than a valuable historical register these results should be very important for the next analysis, which will correlate health data, PM2.5 levels, and sources contributions in the context of the six studied Brazilian metropolises.

Vehicle emissions and PM(2.5) mass concentrations in six Brazilian cities

In Brazil, the principal source of air pollution is the combustion of fuels (ethanol, gasohol, and diesel). In this study, we quantify the contributions that vehicle emissions make to the urban fine particulate matter (PM(2.5)) mass in six state capitals in Brazil, collecting data for use in a larger project evaluating the impact of air pollution on human health. From winter 2007 to winter 2008, we collected 24-h PM(2.5) samples, employing gravimetry to determine PM(2.5) mass concentrations; reflectance to quantify black carbon concentrations; X-ray fluorescence to characterize elemental composition; and ion chromatography to determine the composition and concentrations of anions and cations. Mean PM(2.5) concentrations in the cities of São Paulo, Rio de Janeiro, Belo Horizonte, Curitiba, Porto Alegre, and Recife were 28, 17.2, 14.7, 14.4, 13.4, and 7.3 μg/m(3), respectively. In São Paulo and Rio de Janeiro, black carbon explained approximately 30% of the PM(2.5) mass. We used receptor models to identify distinct source-related PM(2.5) fractions and correlate those fractions with daily mortality rates. Using specific rotation factor analysis, we identified the following principal contributing factors: soil and crustal material; vehicle emissions and biomass burning (black carbon factor); and fuel oil combustion in industries (sulfur factor). In all six cities, vehicle emissions explained at least 40% of the PM(2.5) mass. Elemental composition determination with receptor modeling proved an adequate strategy to identify air pollution sources and to evaluate their short- and long-term effects on human health. Our data could inform decisions regarding environmental policies vis-à-vis health care costs.

Urban air pollution: a representative survey of PM(2.5) mass concentrations in six Brazilian cities

In urban areas of Brazil, vehicle emissions are the principal source of fine particulate matter (PM(2.5)). The World Health Organization air quality guidelines state that the annual mean concentration of PM(2.5) should be below 10 μg m(-3). In a collaboration of Brazilian institutions, coordinated by the University of São Paulo School of Medicine and conducted from June 2007 to August 2008, PM(2.5) mass was monitored at sites with high traffic volumes in six Brazilian state capitals. We employed gravimetry to determine PM(2.5) mass concentrations, reflectance to quantify black carbon concentrations, X-ray fluorescence to characterize elemental composition, and ion chromatography to determine the composition and concentrations of anions and cations. Mean PM(2.5) concentrations and proportions of black carbon (BC) in the cities of São Paulo, Rio de Janeiro, Belo Horizonte, Curitiba, Recife, and Porto Alegre were 28.1 ± 13.6 μg m(-3) (38% BC), 17.2 ± 11.2 μg m(-3) (20% BC), 14.7 ± 7.7 μg m(-3) (31% BC), 14.4 ± 9.5 μg m(-3) (30% BC), 7.3 ± 3.1 μg m(-3) (26% BC), and 13.4 ± 9.9 μg m(-3) (26% BC), respectively. Sulfur and minerals (Al, Si, Ca, and Fe), derived from fuel combustion and soil resuspension, respectively, were the principal elements of the PM(2.5) mass. We discuss the long-term health effects for each metropolitan region in terms of excess mortality risk, which translates to greater health care expenditures. This information could prove useful to decision makers at local environmental agencies.

Potential health impact of ultrafine particles under clean and polluted urban atmospheric conditions: a model-based study

The main goal of this study was to improve the knowledge of ultrafine particle number distributions in large urban areas and also to call the attention to the importance of these particles on assessing health risks. Measurements of aerosol size distributions were performed during 2 weeks, with distinct pollutant concentrations (polluted and clean periods), on the rooftop of a building located in downtown of the megacity of São Paulo, Brazil. CO, NO(2), PM(10), SO(2), and O(3) concentrations and meteorological variables were also used. Aerosol size distribution measurements showed that geometric mean diameters of the size spectra in the polluted period are on average considerably larger than those in the clean one. Besides the fact that total number of ultrafine particles did not show significant differences, during the polluted period, geometric mean diameter was larger than during the clean one. The results of a mathematical model of particle deposition on human respiratory tract indicated a more significant effect of smaller particles fraction of the spectra, which predominate under clean atmospheric conditions. The results also indicated that urban environmental conditions usually considered good for air quality, under the criteria of low mass concentration, do not properly serve as air quality standard to very small particles. In the size range of ultrafine particles, this traditional clean atmospheric condition can offer a strong risk to pulmonary hazards, since the cleansing of the atmosphere creates good conditions to increase the concentration of nucleation mode particles.