Accurately monitoring atmospheric carbon dioxide (CO₂) is vital for understanding global carbon fluxes and shaping climate mitigation policies. This study explores the seasonal variability of biases between satellite-derived OCO-2 XCO₂ observations and ground-based TCCON XCO₂ measurements at the Caltech TCCON station over nine years (2014–2023). The study categorized the data by observation mode (nadir or glint) and month and investigated the distributions of deviations from the mean bias.Distinct seasonal patterns emerged in the bias variability. Nadir mode observations demonstrated consistent median deviations, ranging from -0.6 to 0.4 ppm, indicating minimal bias variability. In contrast, glint mode observations showed substantial variability, with absolute median deviations surpassing 1 ppm during January, March, and September. Skewness analysis revealed asymmetries in the data distributions and the presence of significant outliers. A strong correlation was observed between monthly Normalized Difference Vegetation Index (NDVI) values and glint mode skewness (R² = 0.76), highlighting its sensitivity to surface reflectance and vegetation dynamics. In comparison, nadir mode skewness demonstrated greater stability with minimal correlation to NDVI.The study underscores the need to consider environmental factors, such as vegetation coverage and observation mode differences when interpreting OCO-2 data. By identifying the role of seasonal variability in satellite-ground measurement discrepancies, these findings contribute to refining retrieval algorithms and enhancing satellite-based XCO₂ monitoring accuracy. Improved accuracy supports the development of more reliable carbon flux models, which are essential for effective climate policy and mitigation strategies. Future studies should replicate this analysis at other TCCON stations and incorporate additional environmental variables to further elucidate the drivers of seasonal biases in OCO-2 observations.

This work examines the impact of the electrification of the Holon-Bat Yam passenger train line (central Israel) on air pollutant concentrations using data collected from air quality monitoring stations that operated at the train stations across the electrified train line. We present statistically significant reduction in the annual average NO2, NO and NOX concentrations (29-45%, 79-85% and 65-75%, respectively), attributed to the electrification of the passenger train line. The drop in the NO and NOX concentrations was much stronger than in the NO2 concentrations, since NO is the main nitrogen species emitted by diesel locomotives. PM2.5 concentrations also significantly decreased, but only in two (out of the three) train stations situated along the electrified line. Following various analyses, we conclude that electrification of train lines reduces train locomotive emissions and improves the air quality at the stations, as expected, thus protecting the passengers and reducing their exposure to air pollutants. Although this study presents a specific case, the findings are expected to be applicable, at least quantitatively, to other locations, as railway electrification removes emissions associated with fossil-fuel-powered locomotives. This work supports railway electrification policy, which has the potential to substantially lower air pollution levels and diminish the passengers' exposure to harmful air pollutants.

The complex relationship between air pollution sources, the meteorological conditions governing their dispersion, and the resulting pollutant concentrations is an interesting scientific topic with significant implications for air resource management. A widely adopted approach to exploring these interactions is the interpretation of statistical models which simulate them. Dispersion conditions can vary markedly-from cold, calm nights with stable atmosphere to stormy periods characterised by intense turbulence and convection. Deeper insights can be obtained by interpreting statistical models trained on data subsets that correspond to specific emission-dispersion conditions. To achieve such distinct subsets, we present a robust and reproducible methodology for partitioning the multidimensional state space, defined by pollution sources and meteorological variables, into a large number of clusters at each monitoring location. Our methodology ensures a systematic and objective analysis across multiple stations. We analyse four years of hourly data, integrating high-resolution meteorological outputs from a numerical weather prediction model with traffic volume data used as proxies for emissions or precursors of NO, NO2, NOx, PM2.5, and O3 concentrations. These pollutants were observed at 85 air quality monitoring stations across Israel. The resulting clusters capture sub-daily temporal patterns that are indicative of distinct emission-dispersion scenarios in the region. We demonstrate that statistical models trained on these clustered subsets consistently outperform models trained on the full-period datasets. This highlights the value of our clustering approach in improving both predictive performance and scientific understanding of air pollution dispersion dynamics.

BACKGROUND AND AIMS:Knowledge is lacking on the relationship between greenness and mortality in cancer survivors who experience coronary artery disease, a cardio-oncologic population. We aimed to investigate the association between residential greenness exposure and all-cause mortality in a cardio-oncologic population. METHODS AND RESULTS:Cancer survivors undergoing percutaneous coronary intervention at the Rabin Medical Center in Israel between 2004 and 2014 were included in the study. Clinical data were collected from medical records during index hospitalization and from the Israeli National Cancer Registry. Residential greenness was estimated by the normalized difference vegetation index (NDVI), a satellite-based index derived from Landsat imagery at a 30-m spatial resolution, with larger values indicating higher levels of vegetative density (ranging between -1 and 1). Mortality follow-up data were obtained through the end of 2021. Cox models were used to assess the hazard ratios (HRs) for all-cause mortality per 1SD increase in NDVI. Among 1331 patients analysed [mean (SD) age, 75.6 (10.2) years, 373 (28%) females], the mean (SD) NDVI within a 300-m radius was 0.12 (0.03). During a median follow-up period of 12.0 (IQR 9.2-14.7) years, 883 (66%) participants died. After adjustment for potential confounding factors, including residential socioeconomic status, air pollution, and smoking, NDVI was inversely associated with mortality hazard [HR (95% CI) = 0.93 (0.86, 0.99); P = 0.042]. The association was stronger among individuals with more recently (<10 years) diagnosed cancer [HR (95% CI) = 0.89 (0.81, 0.98); P = 0.019]. CONCLUSION:In a cohort of cardio-oncologic patients, greenness was independently associated with lower mortality.

Accurate monitoring of atmospheric carbon dioxide (CO2) is essential for understanding carbon fluxes and guiding climate mitigation strategies. This study investigates the seasonal variability of the bias between satellite-based OCO-2 XCO2 observations and ground-based TCCON XCO2 measurements over nine years (2014-2023) at the Caltech TCCON station. Grouping the data by the observation month and mode (nadir or glint) enabled us to analyze the distributions of the deviations from the bias. Our results reveal distinct seasonal patterns in the bias variability. The distributions of the nadir mode observations' deviations from the mean bias exhibited relatively stable medians, ranging from -0.6 to 0.4 ppm, indicating minimal deviation from the mean bias. In contrast, distributions of the glint mode observations' deviations from the mean bias showed significant variability, with the absolute median values exceeding 1 ppm during January, March, and September. Skewness analysis highlighted the asymmetry of the data distributions and the presence of significant outliers, with the nadir mode displaying a notable positive skewness in September and the glint mode demonstrating high negative skewness during periods of elevated vegetation cover. Furthermore, seasonal vegetation dynamics, represented by monthly NDVI values, were strongly correlated with skewness in the glint mode (R-2 = 0.76), underscoring its sensitivity to bright conditions and surface reflectance variability. Nadir mode, in contrast, showed minimal correlation, reflecting its relative stability. These findings emphasize the importance of accounting for operational differences and environmental factors, such as vegetation cycles, when interpreting the OCO-2 data. This study provides novel insights into the temporal dynamics of satellite-ground measurement discrepancies by addressing the role of seasonal variability in OCO-2 biases. Such findings are essential for improving retrieval algorithms, enhancing the accuracy of satellite-based CO2 monitoring, and supporting the development of reliable carbon flux models for climate policy and mitigation efforts. Future work should expand this analysis to other TCCON sites and incorporate additional environmental variables to further refine our understanding of seasonal influences on satellite biases.

This work studies long-term trends of observed meteorological parameters and of exposure to excessive heat over 74 years in Israel (1950–2023). We report an increasing trend of recurring exposure of the Israeli population to excessive heat during most of the summer noon hours, with the heat index often above the physiologically no-risk threshold. Specifically, since the beginning of the millennium, a significant increase in summertime decadal means of ambient noontime temperature (Ta), absolute humidity (AH), and heat index (HI) is evident relative to the 1950’s (Ta: 0.06 °C/year, AH: 0.06 g/m3year, HI: 0.09 °C/year). The experienced increase summertime thermal discomfort by the Israeli population results from the significant and synergistic increase in co-exposure to ambient temperature and humidity. Indeed, long-term satellite data (Landsat 1984–2021) of the east Mediterranean Sea Surface Temperature (SST) reveal a significant change (SST: 0.05 °C/year), which elucidates the corresponding increase in the absolute humidity. Leishmaniasis is a climate-related vector-borne infectious disease. However, the 1956–2017 leishmaniasis incidence rates in Israel do not correlate with the significant increase in the ambient temperature and heat index, representing development of climate resilience in terms of administrated prevention measures (namely, systematic adaptation) to this climate-related disease.

Fine particulate matter (PM2.5) is a complex mixture of aerosol particles with varying properties and sources, both local and distant. In areas lacking detailed monitoring of PM2.5 speciation, the common source-apportionment analyses are not applicable. This study demonstrates an alternative framework for estimating sources and processes that affect observed PM2.5 concentrations when information on the particle composition is unavailable. Eight years (2012-2019) of half-hourly PM2.5 observations from 10 air quality monitoring (AQM) stations, clustered according to their airmass transport sector were analyzed, using Non-negative Matrix Factorization (NMF). Factors were determined based on their variation in time, space, and between airmass sectors. Employing a supervised machine-learning model provided insights into the relationships between the extracted factors, meteorological parameters and co-measured airborne pollutants. Factor interpretations were evaluated through comparisons with measurements of PM2.5 species from a nearby Surface PARTiculate mAtter Network (SPARTAN) station. The NMF successfully separated background factors from an urban anthropogenic-activity factor, with the latter accounting for approximately 60 % of the observed PM2.5 levels in Tel Aviv (similar to 10 +/- 6 mu g/m(3)). Positive monotonic relationships were observed between the PM2.5 urban anthropogenic-activity factor and measurements of nitrogen oxides (NOx) and absolute humidity (AH), representing the impact of traffic emissions and hygroscopic growth, respectively. The summer background factor was found to represent long-range transport (LRT) from Europe, showing a good agreement (R-2 = 0.81) with ammonium sulphate concentrations. Our results demonstrate that a spatial NMF analysis can reliably estimate contributions of different sources with distinct compositions and properties to the total observed PM2.5. Using such an analysis, future environmental health studies could assess health risks associated with exposure to distinct PM2.5 fractions. This information may assist decision makers to set environmental targets for abating PM2.5 with specific compositions and properties.

Understanding the role of meteorology in determining air pollutant concentrations is an important goal for better comprehension of air pollution dispersion and fate. It requires estimating the strength of the causal associations between all the relevant meteorological variables and the pollutant concentrations. Unfortunately, many of the meteorological variables are not routinely observed. Furthermore, the common analysis methods cannot establish causality. Here we use the output of a numerical weather prediction model as a proxy for real meteorological data, and study the causal relationships between a large suite of its meteorological variables, including some rarely observed ones, and the corresponding nitrogen dioxide (NO2) concentrations at multiple observation locations. Time-lagged convergent cross mapping analysis is used to ascertain causality and its strength, and the Pearson and Spearman correlations are used to study the direction of the associations. The solar radiation, temperature lapse rate, boundary layer height, horizontal wind speed and wind shear were found to be causally associated with the NO2 concentrations, with mean time lags of their maximal impact at -3, -1, -2 and -3 hours, respectively. The nature of the association with the vertical wind speed was found to be uncertain and region- dependent. No causal association was found with relative humidity, temperature and precipitation.

Vehicle -emitted fine particulate matter (PM 2.5 ) has been associated with significant health outcomes and environmental risks. This study estimates the contribution of traffic -related exhaust emissions (TREE) to observed PM 2.5 using a novel factorization framework. Specifically, co -measured nitrogen oxides (NO x ) concentrations served as a marker of vehicle -tailpipe emissions and were integrated into the optimization of a Non -negative Matrix Factorization (NMF) analysis to guide the factor extraction. The novel TREE-NMF approach was applied to long-term (2012 - 2019) PM 2.5 observations from air quality monitoring (AQM) stations in two urban areas. The extracted TREE factor was evaluated against co -measured black carbon (BC) and PM 2.5 species to which the TREE-NMF optimization was blind. The contribution of the TREE factor to the observed PM 2.5 concentrations at an AQM station from the first location showed close agreement ( R 2 = 0 .79) with monitored BC data. In the second location, a comparison of the extracted TREE factor with measurements at a nearby Surface PARTiculate mAtter Network (SPARTAN) station revealed moderate correlations with PM 2.5 species commonly associated with fuel combustion, and a good linear regression fit with measured equivalent BC concentrations. The estimated concentrations of the TREE factor at the second location accounted for 7 - 11 % of the observed PM 2.5 in the AQM stations. Moreover, analysis of specific days known to be characterized by little traffic emissions suggested that approximately 60 - 78 % of the traffic -related PM 2.5 concentrations could be attributed to particulate traffic -exhaust emissions. The methodology applied in this study holds great potential in areas with limited monitoring of PM 2.5 speciation, in particular BC, and its results could be valuable for both future environmental health research, regional radiative forcing estimates, and promulgation of tailored regulations for traffic -related air pollution abatement.

Abstract Background Evidence suggests an inverse association between surrounding greenness and mortality among the general population and individuals with coronary heart disease (CHD). Little is known whether greenness-related mortality reduction differs between CHD and CHD-free individuals. Aim To compare associations of greenness and all-cause mortality between individuals with and without CHD. Methods Data from four Israeli cohorts were utilized: Two population-based cohorts derived from two national health surveys (n=3,246, inception years 1999–2001; n=1,799, 2005–2006) and two CHD patient-based cohorts (n=1,521, 1992–1993; n=12,784, 2004–2014). The two latter comprised patients hospitalized with an acute coronary syndrome (ACS, i.e., acute myocardial infarction or unstable angina pectoris) or stable coronary artery disease (i.e., undergoing percutaneous coronary intervention without ACS indication). Participants who self-reported preexisting CHD at baseline in the general population cohorts were excluded. Exposure to residential greenness was estimated using the Normalized Difference Vegetative Index (NDVI), a satellite image-based measure ranging from –1.0 to 1.0, with larger values indicating higher levels of vegetation density. NDVI was calculated within 100m, 300m, and 800m radii around each participant’s home address and averaged over the entire follow-up period. Data on all-cause mortality (last update: 2018) was retrieved from national registries. Cox models were constructed to assess association overall and by vulnerability level (CHD-free, stable disease, and ACS). All models were adjusted for harmonized covariates across cohorts, including age, sex, ethnicity, neighborhood socio-economic status, smoking, diabetes, hypertension, stroke, and year of study entry. Results A total of 17,217 participants were included in the study [mean (SD) age, 63.8 (15.3); 42.3% women]. Among them, 3,778 (22%) were free of CHD at baseline, 4,762 (28%) had stable disease, and 8,677 (50%) had ACS. During a median [interquartile range (IQR)] follow-up of 9 (5–12) years, 4,736 deaths occurred. The mean (range) period NDVI in the four cohorts was 0.11 (0.01–0.25). In the pooled analysis, an IQR increase in 300m-NDVI was associated with an adjusted hazard ratio (HR) of 0.93 [95% confidence interval (CI) 0.89, 0.97) for mortality. Stratification by vulnerability level showed heterogeneous results (p=0.03). While there was no association among CHD-free individuals (HR=1.11, 95% CI 0.98, 1.26), an inverse relationship was observed among CHD individuals, with a stronger association among ACS compared with stable disease individuals (ACS: HR=0.92, 95% CI 0.87, 0.98 vs. Stable: HR=0.95, 95% CI 0.87, 1.03). A similar pattern was seen across the different radii zones. Conclusion Residential green spaces exhibit a stronger association with reduced mortality risk in individuals with CHD compared to those without CHD, especially noticeable among ACS patients.

Background: There is substantial public concern about excess health risks posed by residential proximity to petrochemical industries, particularly regarding cancer. In the Haifa Bay Area (HBA), which contains Israel’s densest industrial area, these concerns are strengthened by elevated age- and sex-adjusted cancer mortality rates compared to other areas in Israel since the late 1960s. We aimed to study the association between adolescent exposure to industrial air pollution in the HBA with adult-onset cancer. Methods: This is a historical cohort study. The study population comprised 2,187,317 subjects, using the Israeli medical corps data linked to the Israel National Cancer Registry with follow-up of up to 45 years. Exposure assessments were estimated by a kriging interpolation model. Cancer risk was assessed using crude and multivariable Cox proportional hazards models.Results: We found increased crude (HR=1.23, 95%CI= 1.17 to 1.29) and adjusted (HR=1.16, 95%CI=1.10 to 1.21) of generalized risk of cancer with increased exposure to air pollution in HBA. The associations remained robust in analyses stratified by birth decade and SES, and also in those restricted to subjects with unimpaired health at baseline. We found evidence of increased risk in five of 13 specific cancer categories for which monotonic associations are evident (leukemia, melanoma, female breast, CNS, and thyroid tumors). Conclusions: Our findings strengthen the hypothesis that these industrial exposures posed a carcinogenic risk during the study period. This has major public health implications for this highly populated area itself and for other populations exposed to similar industries worldwide.

There is substantial public concern about the health risks of proximity to petrochemical industries. In the Haifa Bay Area (HBA), which contains Israel's densest industrial area, these concerns have been strengthened by elevated cancer mortality rates since the late 1960s. We studied the association between adolescent exposure to industrial air pollution in the HBA and adult-onset cancer. This is a historical cohort study. The study population comprised 2,187,317 subjects, using the Israeli medical corps data linked to the Israel National Cancer Registry with follow-up of up to 45 years. Exposure assessments were estimated by a spatial kriging interpolation model of SO2, serving as a marker for the dispersion of air pollution emitted from the complex during the study period. We found increased crude (HR = 1.23, 95%CI= 1.17 to 1.29) and adjusted (HR = 1.16, 95%CI = 1.10 to 1.21) risk of cancer with increased exposure to air pollution in HBA. The associations remained robust in analyses stratified by decade and socio-economic status. We found evidence of monotonically increased risk in five of 13 cancer categories (leukemia, melanoma, female breast, central nervous system, and thyroid tumors). Our findings strengthen the hypothesis that this exposure posed a carcinogenic risk during the study period.

Background: Evidence regarding environmental exposure to green spaces and outcomes in coronary disease patients is lacking. We evaluated the association between residential exposure to greenness and mortality in patients undergoing percutaneous coronary interventions (PCI). Methods: Consecutive patients undergoing PCI at the Rabin Medical Center in Israel between 2004-2014 (n = 12,104) were studied. Clinical data at the time of hospitalization were extracted from medical records. Mortality data (through 2017) were obtained from the Ministry of Health. Patients with incomplete information on residential addresses were excluded. Exposure to greenness was estimated using normalized difference vegetation index (NDVI), a satellite-based index derived from Landsat 30 m spatial resolution imagery, with larger values indicating higher levels of vegetative density. NDVI was estimated within a buffer of 300 m around each patient’s home and as the point value of each 30 m pixel (immediate living environment). Additional residential-based environmental measures were obtained. Cox models assessed the hazard ratios (HRs) for mortality associated with greenness measures. Results: Among 11,262 patients analyzed [median age, 69 (IQR 61-78) y, 24% women], median NDVI-300 was 0.15 (IQR 0.13-0.17) and median NDVI-30 was 0.14 (IQR 0.11-0.17). Patients with higher NDVI-300 were slightly older; NDVI-30 was inversely correlated to ambient air pollution. During a median follow-up of 8.1 (IQR 5.1-10.6) years, 3,217 participants died. After adjustment for sociodemographic and clinical factors, NDVI-30 -but not NDVI-300- was associated with lower mortality (Figure), with an HR of 0.96 (95% CI: 0.92-0.99) per 1 SD increase. Conclusions: In this PCI registry, residential exposure to green spaces in the immediate living environment was associated with lower mortality. Results for the extended area of the living environment were inconclusive. This inconsistency between different spatial resolutions warrants further investigation

BACKGROUND Childhood overweight and obesity is a global public health problem. Rapid infant weight gain is predictive of childhood overweight. Studies found that exposure to ambient air pollution is associated with childhood overweight, and have linked prenatal exposure to air pollution with rapid infant weight gain. OBJECTIVES To examine the association between prenatal and postnatal ambient NO2 exposure, a traffic-related marker, with rapid weight gain in infants. METHODS We carried out a population-based historical cohort study using data from the Israeli national network of maternal and child health clinics. The study included 474,136 infants born at term with birthweight ≥2500 g in 2011-2019 in central Israel. Weekly averages of NO2 concentration throughout pregnancy (prenatal) and the first 4 weeks of life (postnatal) were assessed using an optimized dispersion model and were linked to geocoded home addresses. We modelled weight gain velocity throughout infancy using the SuperImposition by Translation and Rotation (SITAR) method, a mixed-effects nonlinear model specialized for modelling growth curves, and defined rapid weight gain as the highest velocity tertile. Distributed-lag models were used to assess critical periods of risk and to measure relative risks for rapid weight gain. Adjustments were made for socioeconomic status, population group, subdistrict, month and year of birth, and the alternate exposure period - prenatal or postnatal. RESULTS The cumulative adjusted relative risk for rapid weight gain of NO2 exposure was 1.02 (95% confidence intereval [CI] 1.00, 1.04) for exposure throughout pregnancy and 1.02 (95% CI 1.01, 1.04) for exposure during the first four postnatal weeks per NO2 interquartile range increase (7.3 ppb). An examination of weekly associations revealed that the critical period of risk for the prenatal exposure was from mid-pregnancy to birth. CONCLUSIONS Prenatal and postnatal exposures to higher concentrations of traffic-related air pollution are each independently associated with rapid infant weight gain, a risk factor for childhood overweight and obesity.

Fine airborne particles (diameter <2.5 mu m; PM2.5) are recognized as a major threat to human health due to their physicochemical properties: composition, size, shape, etc. However, normally only size-fraction-specific particle concentrations are monitored. Interestingly, although the aerosol type is reported as part of the aerosol optical depth retrieval from satellite observations, it has not been utilized, to date, as an auxiliary information/co-variate for PM2.5 prediction. We developed Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) models that account for this information when predicting surface PM2.5. The models take as input only widely available data: satellite aerosol products with full cover and surface meteorological data. Distinct models were developed for AOD of specific aerosol types. Both the RF and XGBoost models performed well, showing moderate-to-high crossvalidated adjusted R2 (RF: 0.753-0.909; XGBoost: 0.741-0.903), depending on the aerosol type and other covariates. The weighted performance of the specific aerosol-type models was higher than of the RF and XGBoost baseline models, where all the AOD retrievals were used together (the common practice). Our approach can provide improved risk estimates due to exposure to PM2.5, better resolved radiative forcing calculations, and tailored abatement surveillance of specific pollutants/sources.

Exposure to excessive heat can lead to adverse health outcomes in both healthy and vulnerable individuals. This study examines the spatiotemporal variability of exposure to severe heat at the sub-neighborhood scale using temperature and relative humidity measurements of a wireless distributed sensor network (WDSN). First, we demonstrate a multi-sensor calibration scheme for the temperature and the relative humidity sensors. Next, exposure to heat was calculated using the heat index (HI) scale, which enables linking exposure to HI and heat-related health risks. We noticed repeated exposures to excessive heat above the safe threshold for about 8 h per day throughout July–August, 2015, in Haifa, Israel. Persistent exposure to such conditions is unhealthy. The areas that experienced high HI were scattered across the study area, with the HI showing spatiotemporal variability. In general, in some microenvironments, the HI peaked earlier during the day than in other microenvironments. This was attributed to variability in urban physical drivers, which were found to be good predictors of the morning HI variability buildup but less so of the HI variability in the afternoon. Our results are consistent with summer HI occurrence in the study area in the past 20 years. Since exposure to excessive heat in the east Mediterranean is expected to increase in the future due to climate changes, it may result in a grave health toll.

Background: Studies assessing the associations between prenatal air pollution exposures and birth outcomes commonly use maternal addresses at the time of delivery as a proxy for residency throughout pregnancy. Yet, in large-scale epidemiology studies, maternal addresses commonly originate from an administrative source. Objective: This study aimed to examine the use of population registry addresses to assign exposure estimations and to evaluate the impact of inaccurate addresses on exposure estimates and association measures of prenatal exposures with congenital hypothyroidism. Methods: We used morbidity data for congenital hypothyroidism from the national program for neonatal screening for 2009-2015 and address data from two sources: population registry and hospital records. We selected neonates with geocoded addresses from both sources (N = 685,491) and developed a comparison algorithm for these addresses. Next, we assigned neonates with exposures from ambient air pollution of PM and NO2/NOX, evaluated exposure assessment differences, and used multivariable logistic regression models to assess the impact that these differences have on association measures.Results: We found that most of the exposure differences between neonates with addresses from both sources were around zero and had a leptokurtic distribution density, with most values being zero. Additionally, associations between exposure and congenital hypothyroidism were comparable, regardless of address source and when we limited the model to neonates with identical addresses. Conclusions: We found that ignoring residential inaccuracies results in only a small bias of the associations towards the null. These results strengthen the validity of addresses from population registries for exposure assessment, when detailed residential data during pregnancy are not available.

This study examines uncertainties in the retrieval of the Aerosol Optical Depth (AOD) for different aerosol types, which are obtained from different satellite-borne aerosol retrieval products over North Africa, California, Germany, and India and Pakistan in the years 2007–2019. In particular, we compared the aerosol types reported as part of the AOD retrieval from MODIS/MAIAC and CALIOP, with the latter reporting richer aerosol types than the former, and from the Ozone Monitoring Instrument (OMI) and MODIS Deep Blue (DB), which retrieve aerosol products at a lower spatial resolution than MODIS/MAIAC. Whereas MODIS and OMI provide aerosol products nearly every day over of the study areas, CALIOP has only a limited surface footprint, which limits using its data products together with aerosol products from other platforms for, e.g., estimation of surface particulate matter (PM) concentrations. In general, CALIOP and MAIAC AOD showed good agreement with the AERONET AOD (r: 0.708, 0.883; RMSE: 0.317, 0.123, respectively), but both CALIOP and MAIAC AOD retrievals were overestimated (36–57%) with respect to the AERONET AOD. The aerosol type reported by CALIOP (an active sensor) and by MODIS/MAIAC (a passive sensor) were examined against aerosol types derived from a combination of satellite data products retrieved by MODIS/DB (Angstrom Exponent, AE) and OMI (Aerosols Index, AI, the aerosol absorption at the UV band). Together, the OMI-DB (AI-AE) classification, which has wide spatiotemporal cover, unlike aerosol types reported by CALIOP or derived from AERONET measurements, was examined as auxiliary data for a better interpretation of the MAIAC aerosol type classification. Our results suggest that the systematic differences we found between CALIOP and MODIS/MAIAC AOD were closely related to the reported aerosol types. Hence, accounting for the aerosol type may be useful when predicting surface PM and may allow for the improved quantification of the broader environmental impacts of aerosols, including on air pollution and haze, visibility, climate change and radiative forcing, and human health.