Assessment of primary and secondary ambient particle trends using satellite aerosol optical depth and ground speciation data in the New England region, United States

Evaluating TROPOMI and MODIS performance to capture the dynamic of air pollution in São Paulo state: A case study during the COVID-19 outbreak

Atmospheric pollutant data retrieved through satellite sensors are continually used to assess changes in air quality in the lower atmosphere. During the COVID-19 pandemic, several studies started to use satellite measurements to evaluate changes in air quality in many different regions worldwide. However, although satellite data is continuously validated, it is known that its accuracy may vary between monitored areas, requiring regionalized quality assessments. Thus, this study aimed to evaluate whether satellites could measure changes in the air quality of the state of São Paulo, Brazil, during the COVID-19 outbreak; and to verify the relationship between satellite-based data [Tropospheric NO₂ column density and Aerosol Optical Depth (AOD)] and ground-based concentrations [NO₂ and particulate material (PM; coarse: PM₁₀ and fine: PM_2.5)]. For this purpose, tropospheric NO₂ obtained from the TROPOMI sensor and AOD retrieved from MODIS sensor data by using the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm were compared with concentrations obtained from 50 automatic ground monitoring stations. The results showed low correlations between PM and AOD. For PM₁₀, most stations showed correlations lower than 0.2, which were not significant. The results for PM_2.5 were similar, but some stations showed good correlations for specific periods (before or during the COVID-19 outbreak). Satellite-based Tropospheric NO₂ proved to be a good predictor for NO₂ concentrations at ground level. Considering all stations with NO₂ measurements, correlations >0.6 were observed, reaching 0.8 for specific stations and periods. In general, it was observed that regions with a more industrialized profile had the best correlations, in contrast with rural areas. In addition, it was observed about 57% reductions in tropospheric NO₂ throughout the state of São Paulo during the COVID-19 outbreak. Variations in air pollutants were linked to the region economic vocation, since there were reductions in industrialized areas (at least 50% of the industrialized areas showed >20% decrease in NO₂) and increases in areas with farming and livestock characteristics (about 70% of those areas showed increase in NO₂). Our results demonstrate that Tropospheric NO₂ column densities can serve as good predictors of NO₂ concentrations at ground level. For MAIAC-AOD, a weak relationship was observed, requiring the evaluation of other possible predictors to describe the relationship with PM. Thus, it is concluded that regionalized assessment of satellite data accuracy is essential for assertive estimates on a regional/local level. Good quality information retrieved at specific polluted areas does not assure a worldwide use of remote sensor data.

Drought and ozone air quality in California: Identifying susceptible regions in the preparedness of future drought

California experienced extreme and prolonged drought conditions during the early 2010s. To date, little is known regarding the influence of drought on air quality. Our study quantified site-specific associations between drought (defined by the Standardized Precipitation-Evapotranspiration Index; SPEI) and daily maximum 8-h ozone (O₃) concentrations for California, USA, and then pooled these associations for the years 2009-2015. Overall, ambient O₃ concentration was higher during droughts by 1.18 ppb (95% confidence interval (CI) = 1.00-1.36). The sensitivity of O₃ to drought was greater during the warm season than during the cool season (1.73 ppb versus 0.79 ppb higher O₃ during droughts) with substantial regional variation. In a pooled analysis with meteorological parameters as potential effect modifiers, the spatial heterogeneity of drought-O₃ associations was explained strongly by average relative humidity for each season (71.9% (warm season) and 73.4% (cool season) of the drought-O₃ associations explained), followed by the drought-related changes in relative humidity (47.6% (warm season)) and temperature (53.6% (cool season)). The pooled regression further identified regions susceptible for drought-related O₃ increases as those with relatively low average relative humidity (10-25^th percentiles or 44.3-47.3%) and larger drought-related decrease in relative humidity and increase in temperature. As the drought events are projected to occur with increased frequency and intensity in the era of climate change, the excess health burdens from O₃ exposures attributed to the projected drought events need to be taken into account when allocating air quality and health resources. The impacts of O₃ on health during droughts would confound the health burdens from the drought itself.

Biogenic secondary organic aerosol formation in an urban area of eastern central India: Seasonal variation, size distribution and source characterization

Samples of ambient aerosols were collected at an urban site of eastern central India from monsoon to summer 2016-17 for the characterization of biogenic secondary organic aerosols (BSOA). The BSOA tracers derived from isoprene, α/β-pinene and β-caryophyllene in size-distributed aerosols were studied. Concentrations of total SOAI (Isoprene secondary organic aerosols) were found more abundant than α/β-pinene in summer, while contradictory trends were found in the winter season, where SOAM (monoterpene derived SOA) and SOAS (sesquiterpenes derived SOA) were dominated. Size-distribution study revealed that most of the BSOA were formed in the aerosol phase and dominated in fine mode, except cis-pinonic acid. They were formed in the gaseous phase and partitioned onto the aerosol phase. The alkaline nature of mineral dust particles that triggered the adsorption of gaseous species onto pre-existing particles could be the reason for bimodal size distribution with major coarse mode peak and miner fine mode peak. Temporal variations suggest that the BSOA must be derived from terrestrial vegetation and biomass burning. The isoprene SOC (secondary organic carbon) contributed 0.91%, 1.38%, 0.88% and 1.04% to OC during winter, summer, post-monsoon and monsoon season, respectively. The isoprene SOC in fine mode was found to be higher than the coarse mode.

Multi-city comparative PM2.5 source apportionment for fifteen sites in Europe: The ICARUS project

PM_2.5 is an air pollution metric widely used to assess air quality, with the European Union having set targets for reduction in PM_2.5 levels and population exposure. A major challenge for the scientific community is to identify, quantify and characterize the sources of atmospheric particles in the aspect of proposing effective control strategies. In the frame of ICARUS EU2020 project, a comprehensive database including PM_2.5 concentration and chemical composition (ions, metals, organic/elemental carbon, Polycyclic Aromatic Hydrocarbons) from three sites (traffic, urban background, rural) of five European cities (Athens, Brno, Ljubljana, Madrid, Thessaloniki) was created. The common and synchronous sampling (two seasons involved) and analysis procedure offered the prospect of a harmonized Positive Matrix Factorization model approach, with the scope of identifying the similarities and differences of PM_2.5 key-source chemical fingerprints across the sampling sites. The results indicated that the average contribution of traffic exhausts to PM_2.5 concentration was 23.3% (traffic sites), 13.3% (urban background sites) and 8.8% (rural sites). The average contribution of traffic non-exhausts was 12.6% (traffic), 13.5% (urban background) and 6.1% (rural sites). The contribution of fuel oil combustion was 3.8% at traffic, 11.6% at urban background and 18.7% at rural sites. Biomass burning contribution was 22% at traffic sites, 30% at urban background sites and 28% at rural sites. Regarding soil dust, the average contribution was 5% and 8% at traffic and urban background sites respectively and 16% at rural sites. Sea salt contribution was low (1-4%) while secondary aerosols corresponded to the 16-34% of PM_2.5. The homogeneity of the chemical profiles as well as their relationship with prevailing meteorological parameters were investigated. The results showed that fuel oil combustion, traffic non-exhausts and soil dust profiles are considered as dissimilar while biomass burning, sea salt and traffic exhaust can be characterized as relatively homogenous among the sites.

Long-term impact of PM2.5 mass and sulfur reductions on ultrafine particle trends in Boston, MA from 1999 to 2018

Ultrafine particles (UFPs) pose a human health risk as they can penetrate deep into the respiratory system. The Harvard supersite in Boston, MA provides one of the longest time series of UFP concentrations. This study examined the hypothesis that long-term reductions in PM_2.5 mass and sulfur have influenced UFP trends by limiting the ability of UFPs to coagulate onto the accumulation mode via polydisperse coagulation with larger particles. The study used Generalized Additive Models (GAMs) to assess whether changes in PM_2.5 mass and sulfur concentrations resulted in smaller than expected (assuming no change in PM_2.5 mass or sulfur) decreases in daily UFP trends over the 20-year period from 1999 to 2018. The impact of PM_2.5 mass and sulfur changes were represented as UFP penalties. Bootstrapping was applied to calculate standard errors for the different trend and penalty estimates. Results showed that PM_2.5 mass and sulfur concentrations declined significantly over the study period. The analysis found an estimated 7.3% (95% CI: 3.5, 11.1%) UFP penalty due to long-term PM_2.5 mass trends, and a 9.9% (95% CI: 6.2, 13.7%) UFP penalty due to long-term sulfur trends. Findings from this study suggest that future UFP control efforts should account for the role of PM_2.5 mass and sulfur changes.

IMPLICATIONS

Using one of the longest available time series of UFP concentrations (1999 to 2018), this study examined the hypothesis that long-term trends of PM_2.5 mass and sulfur concentrations have an impact on UFP trends. We found that PM_2.5 mass and sulfur reductions had a small but significant impact, i.e., penalty, on UFP trends. Improved understanding of the impact of PM_2.5 mass and sulfur concentrations on UFP trends can inform future air quality control efforts.

PM2.5 source apportionment for the port city of Thessaloniki, Greece

This paper aims to identify the chemical fingerprints of potential PM2.5 sources and estimate their contribution to Thessaloniki port-city's air quality. For this scope, Positive Matrix Factorization model was applied on a comprehensive PM2.5 dataset collected over a one-year period, at two sampling sites: the port and the city center. The model indicated six and five (groups of) sources contributing to particle concentration at the two sites, respectively. Traffic and biomass burning (winter months) comprise the major local PM sources for Thessaloniki (their combined contribution can exceed 70%), revealing two of the major control-demanding problems of the city. Shipping and in-port emissions have a non-negligible impact (average contribution to PM2.5: 9-13%) on both primary and secondary particles. Road dust factor presents different profile and contribution at the two sites (19.7% at the port; 7.4% at the city center). The secondary-particle factor represents not only the aerosol transportation over relatively long distances, but also a part of traffic-related pollution (14% at the port; 34% at the city center). The study aims to contribute to the principal role of quantitative information on emission sources (source apportionment) in port-cities for the implementation of the air quality directives and guidelines for public health.

Applications of Space Technologies to Global Health: Scoping Review

Background

Space technology has an impact on many domains of activity on earth, including in the field of global health. With the recent adoption of the United Nations’ Sustainable Development Goals that highlight the need for strengthening partnerships in different domains, it is useful to better characterize the relationship between space technology and global health.

Objective

The aim of this study was to identify the applications of space technologies to global health, the key stakeholders in the field, as well as gaps and challenges.

Methods

We used a scoping review methodology, including a literature review and the involvement of stakeholders, via a brief self-administered, open-response questionnaire. A distinct search on several search engines was conducted for each of the four key technological domains that were previously identified by the UN Office for Outer Space Affairs’ Expert Group on Space and Global Health (Domain A: remote sensing; Domain B: global navigation satellite systems; Domain C: satellite communication; and Domain D: human space flight). Themes in which space technologies are of benefit to global health were extracted. Key stakeholders, as well as gaps, challenges, and perspectives were identified.

Results

A total of 222 sources were included for Domain A, 82 sources for Domain B, 144 sources for Domain C, and 31 sources for Domain D. A total of 3 questionnaires out of 16 sent were answered. Global navigation satellite systems and geographic information systems are used for the study and forecasting of communicable and noncommunicable diseases; satellite communication and global navigation satellite systems for disaster response; satellite communication for telemedicine and tele-education; and global navigation satellite systems for autonomy improvement, access to health care, as well as for safe and efficient transportation. Various health research and technologies developed for inhabited space flights have been adapted for terrestrial use.

Conclusions

Although numerous examples of space technology applications to global health exist, improved awareness, training, and collaboration of the research community is needed.

Pregnancy and Lifetime Exposure to Fine Particulate Matter and Infant Mortality in Massachusetts, 2001-2007

Many studies have found associations between particulate matter having an aerodynamic diameter of ≤2.5 μm (PM2.5) and adult mortality. Comparatively few studies evaluated particles and infant mortality, although infants and children are particularly vulnerable to pollution. Moreover, existing studies mostly focused on short-term exposure to larger particles. We investigated PM2.5 exposure during pregnancy and lifetime and postneonatal infant mortality. The study included 465,682 births with 385 deaths in Massachusetts (2001-2007). Exposures were estimated from PM2.5-prediction models based on satellite imagery. We applied extended Cox proportional hazards modeling with time-dependent covariates to total, respiratory, and sudden infant death syndrome mortality. Exposure was calculated from birth to death (or end of eligibility for outcome, at age 1 year) and pregnancy (gestation and each trimester). Models adjusted for sex, birth weight, gestational length, season of birth, temperature, relative humidity, and maternal characteristics. Hazard ratios for total, respiratory, and sudden infant death syndrome mortality per-interquartile-range increase (1.3 μg/m3) in lifetime PM2.5 exposure were 2.66 (95% confidence interval (CI): 2.11, 3.36), 3.14 (95% CI: 2.39, 4.13), and 2.50 (95% CI: 1.56, 4.00), respectively. We did not observe a statistically significant relationship between gestational exposure and mortality. Our findings provide supportive evidence that lifetime exposure to PM2.5 increases risk of infant mortality.

Short-Term Exposure to Ambient Air Pollution and Biomarkers of Systemic Inflammation: The Framingham Heart Study

OBJECTIVE

The objective of this study is to examine associations between short-term exposure to ambient air pollution and circulating biomarkers of systemic inflammation in participants from the Framingham Offspring and Third Generation cohorts in the greater Boston area.

APPROACH AND RESULTS

We included 3996 noncurrent smoking participants (mean age, 53.6 years; 54% women) who lived within 50 km from a central air pollution monitoring site in Boston, MA, and calculated the 1- to 7-day moving averages of fine particulate matter (diameter<2.5 µm), black carbon, sulfate, nitrogen oxides, and ozone before the examination visits. We used linear mixed effects models for C-reactive protein and tumor necrosis factor receptor 2, which were measured up to twice for each participant; we used linear regression models for interleukin-6, fibrinogen, and tumor necrosis factor α, which were measured once. We adjusted for demographics, socioeconomic position, lifestyle, time, and weather. The 3- to 7-day moving averages of fine particulate matter (diameter<2.5 µm) and sulfate were positively associated with C-reactive protein concentrations. A 5 µg/m³ higher 5-day moving average fine particulate matter (diameter<2.5 µm) was associated with 4.2% (95% confidence interval: 0.8, 7.6) higher circulating C-reactive protein. Positive associations were also observed for nitrogen oxides with interleukin-6 and for black carbon, sulfate, and ozone with tumor necrosis factor receptor 2. However, black carbon, sulfate, and nitrogen oxides were negatively associated with fibrinogen, and sulfate was negatively associated with tumor necrosis factor α.

CONCLUSIONS

Higher short-term exposure to relatively low levels of ambient air pollution was associated with higher levels of C-reactive protein, interleukin-6, and tumor necrosis factor receptor 2 but not fibrinogen or tumor necrosis factor α in individuals residing in the greater Boston area.

Enhancing the Applicability of Satellite Remote Sensing for PM2.5 Estimation Using MODIS Deep Blue AOD and Land Use Regression in California, United States

We estimated daily ground-level PM2.5 concentrations combining Collection 6 deep blue (DB) Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) data (10 km resolution) with land use regression in California, United States, for the period 2006-2012. The Collection 6 DB method for AOD provided more reliable data retrievals over California's bright surface areas than previous data sets. Our DB AOD and PM2.5 data suggested that the PM2.5 predictability could be enhanced by temporally varying PM2.5 and AOD relations at least at a seasonal scale. In this study, we used a mixed effects model that allowed daily variations in DB AOD-PM2.5 relations. Because DB AOD might less effectively represent local source emissions compared to regional ones, we added geographic information system (GIS) predictors into the mixed effects model to further explain PM2.5 concentrations influenced by local sources. A cross validation (CV) mixed effects model revealed reasonably high predictive power for PM2.5 concentrations with R(2) = 0.66. The relations between DB AOD and PM2.5 considerably varied by day, and seasonally varying effects of GIS predictors on PM2.5 suggest season-specific source emissions and atmospheric conditions. This study indicates that DB AOD in combination with land use regression can be particularly useful to generate spatially resolved PM2.5 estimates. This may reduce exposure errors for health effect studies in California. We expect that more detailed PM2.5 concentration patterns can help air quality management plan to meet air quality standards more effectively.

"What We Breathe Impacts Our Health: Improving Understanding of the Link between Air Pollution and Health"

Air pollution contributes to the premature deaths of millions of people each year around the world, and air quality problems are growing in many developing nations. While past policy efforts have succeeded in reducing particulate matter and trace gases in North America and Europe, adverse health effects are found at even these lower levels of air pollution. Future policy actions will benefit from improved understanding of the interactions and health effects of different chemical species and source categories. Achieving this new understanding requires air pollution scientists and engineers to work increasingly closely with health scientists. In particular, research is needed to better understand the chemical and physical properties of complex air pollutant mixtures, and to use new observations provided by satellites, advanced in situ measurement techniques, and distributed micro monitoring networks, coupled with models, to better characterize air pollution exposure for epidemiological and toxicological research, and to better quantify the effects of specific source sectors and mitigation strategies.

Short-Term Exposure to Air Pollution and Biomarkers of Oxidative Stress: The Framingham Heart Study

BACKGROUND

Short-term exposure to elevated air pollution has been associated with higher risk of acute cardiovascular diseases, with systemic oxidative stress induced by air pollution hypothesized as an important underlying mechanism. However, few community-based studies have assessed this association.

METHODS AND RESULTS

Two thousand thirty-five Framingham Offspring Cohort participants living within 50 km of the Harvard Boston Supersite who were not current smokers were included. We assessed circulating biomarkers of oxidative stress including blood myeloperoxidase at the seventh examination (1998-2001) and urinary creatinine-indexed 8-epi-prostaglandin F2α (8-epi-PGF2α) at the seventh and eighth (2005-2008) examinations. We measured fine particulate matter (PM2.5), black carbon, sulfate, nitrogen oxides, and ozone at the Supersite and calculated 1-, 2-, 3-, 5-, and 7-day moving averages of each pollutant. Measured myeloperoxidase and 8-epi-PGF2α were loge transformed. We used linear regression models and linear mixed-effects models with random intercepts for myeloperoxidase and indexed 8-epi-PGF2α, respectively. Models were adjusted for demographic variables, individual- and area-level measures of socioeconomic position, clinical and lifestyle factors, weather, and temporal trend. We found positive associations of PM2.5 and black carbon with myeloperoxidase across multiple moving averages. Additionally, 2- to 7-day moving averages of PM2.5 and sulfate were consistently positively associated with 8-epi-PGF2α. Stronger positive associations of black carbon and sulfate with myeloperoxidase were observed among participants with diabetes than in those without.

CONCLUSIONS

Our community-based investigation supports an association of select markers of ambient air pollution with circulating biomarkers of oxidative stress.

Satellite remote sensing in epidemiological studies

PURPOSE OF REVIEW

Particulate matter air pollution is a ubiquitous exposure linked with multiple adverse health outcomes for children and across the life course. The recent development of satellite-based remote-sensing models for air pollution enables the quantification of these risks and addresses many limitations of previous air pollution research strategies. We review the recent literature on the applications of satellite remote sensing in air quality research, with a focus on their use in epidemiological studies.

RECENT FINDINGS

Aerosol optical depth (AOD) is a focus of this review and a significant number of studies show that ground-level particulate matter can be estimated from columnar AOD. Satellite measurements have been found to be an important source of data for particulate matter model-based exposure estimates, and recently have been used in health studies to increase the spatial breadth and temporal resolution of these estimates.

SUMMARY

It is suggested that satellite-based models improve our understanding of the spatial characteristics of air quality. Although the adoption of satellite-based measures of air quality in health studies is in its infancy, it is rapidly growing. Nevertheless, further investigation is still needed in order to have a better understanding of the AOD contribution to these prediction models in order to use them with higher accuracy in epidemiological studies.

Interlab comparison of elemental analysis for low ambient urban PM2.5 levels

There is growing concern about the accuracy of trace elemental analysis of ambient particulate matter (PM) samples. This has become important because ambient PM concentrations have decreased over the years, and the lower filter loadings result in difficulties in accurate analysis. The performance of energy-dispersive X-ray reflectance spectrometry was evaluated at Harvard School of Public Health using several methodologies, including intercomparison between two other laboratories. In reanalysis of standard films as unknown samples following calibration, the HSPH ED XRF measurements represented good performance: 2% errors in precision and 4% errors in accuracy. Replicate analysis of ambient air filters with low PM2.5 levels indicated that S, K, Fe, and Ca showed excellent reproducibility, most other quantifiable elements were below 15% error, and the elements with larger percent of flagged measurements had less in precision. Results from the interlaboratory comparison demonstrated that most quantifiable elements, except Na and Al, were quite comparable for the three laboratories. Na performance could be validated from the stoichiometry of Na to Cl of indoor PM2.5 filter samples.