Satellite-based Measurements Research Articles

Satellite-based measurements of temporal and spatial variations in C fluxes of irrigated and rainfed cotton grown in India

The small number of carbon dioxide (CO2) observation networks and the prohibitively high equipment cost restrict the estimation of net ecosystem CO2 exchange (NEE). Satellite-based remote sensing techniques have made it possible to obtain NEE and component carbon (C) fluxes. One-third of the world's cotton area is in India, but the information on NEE is limited. We used the Level 4 Carbon (L4_C) product from the Soil Moisture Active Passive (SMAP) mission to estimate C fluxes based on satellite-derived soil moisture, weather, and vegetation data. For our study (2018-19 to 2020-21), we chose two ecosystems (rainfed central India vs. irrigated northern India). Seasonal variations were observed in C fluxes. Gross primary productivity was the highest during the boll formation phase. This phase was the strongest sink and coincided with the highest CO2 uptake, followed by the flowering and square formation phases. The cotton crop was a C source during the initial vegetative phase and after the boll opening. Overall, the cotton crop was a sink for atmospheric CO2 with an average NEE value of −189.6 g C m−2 under irrigated and −245.6 g C m−2 in rainfed cotton. Higher ecosystem respiration in irrigated cotton resulted in lower C sink strength than rainfed cotton. Our studies indicate that the SMAP L4_C product model estimates can be used to obtain information on C fluxes in real-world situations. Moreover, such satellite-based remote sensing techniques will enable large-scale environmental monitoring with different cropping systems and support policymaking.

Space-based observations of tropospheric ethane map emissions from fossil fuel extraction

Ethane is the most abundant non-methane hydrocarbon in the troposphere, where it impacts ozone and reactive nitrogen and is a key tracer used for partitioning emitted methane between anthropogenic and natural sources. However, quantification has been challenged by sparse observations. Here, we present a satellite-based measurement of tropospheric ethane and demonstrate its utility for fossil-fuel source quantification. An ethane spectral signal is detectable from space in Cross-track Infrared Sounder (CrIS) radiances, revealing ethane signatures associated with fires and fossil fuel production. We use machine-learning to convert these signals to ethane abundances and validate the results against surface observations (R2 = 0.66, mean CrIS/surface ratio: 0.65). The CrIS data show that the Permian Basin in Texas and New Mexico exhibits the largest persistent ethane enhancements on the planet, with regional emissions underestimated by seven-fold. Correcting this underestimate reveals Permian ethane emissions that represent at least 4-7% of the global fossil-fuel ethane source.

Short-term exposure to fine particulate pollution and elderly mortality in Chile

Exposure to fine particulate matter (PM2.5) is known to cause adverse health outcomes. Most of the evidence has been derived from developed countries, with lower pollution levels and different demographics and comorbidities from the rest of the world. Here we leverage new satellite-based measurements of PM2.5, combined with comprehensive public records in Chile, to study the effect of PM2.5 pollution on elderly mortality. We find that a 10 μg/m3 monthly increase in PM2.5 exposure is associated with a 1.7% increase (95% C.I.: 1.1–2.4%) in all-cause mortality for individuals aged 75+. Satellite-based measurements allow us to comprehensively investigate heterogeneous effects. We find remarkably similar effect sizes across baseline exposure, rural and urban areas, income, and over time, demonstrating consistency in the evidence on mortality effects of PM2.5 exposure. The most notable source of heterogeneity is geographical, with effects closer to 5% in the center-south and in the metropolitan area.

Identification of an optimal ground-based validation site for FLEX and quantification of uncertainties using airborne HyPlant data - A case study in Italy

The Fluorescence Explorer (FLEX) satellite will carry the high-resolution Fluorescence Imaging Spectrometer (FLORIS) that measures the complete fluorescence spectrum emitted by chlorophyll of terrestrial vegetation. This small signal must be validated. One validation approach is comparing the fluorescence signal retrieved from satellite-based measurements with ground based measurements. However, the difference in spatial resolution of the satellite and ground-based instruments and a geolocation mismatch will result in differences in the detected signal and thus, in uncertainties of the validation strategy. In a case study, we identify a representative ground site for validating the fluorescence signal by analyzing surface reflectance measurements from an aeroplane.We define requirements of representativeness for a validation ground site in vegetated areas. Based on those requirements, we identify a suitable position within a case study in central Italy using surface reflectance data from the airborne High-Performance Airborne Imaging Spectrometer (HyPlant) measured in summer 2018. The representativeness is quantified by the relative difference between the single HyPlant pixel representing a ground-based measurement and the averaged signal of several HyPlant pixels that mimics a FLORIS pixel. With this measure, we quantify the validation uncertainty due to spatial resolution and geolocation mismatch. The effect of the temporal evolution of the surface properties on the validation uncertainty due to spatial resolution is investigated.We select the ground site position by minimizing the validation uncertainty due to spatial resolution. Especially for wavelengths larger than 700 nm, this uncertainty is smaller than 2 % for all different reference areas. The largest differences between ground-based like measurement and satellite-like measurement of the surface reflectance is due to geolocation mismatch. The uncertainty due the geolocation mismatch is very large for wavelengths smaller than 720 nm and moderate for wavelengths larger than 720 nm. Thus, the surface reflectance at the chosen position for the validation site is not homogeneous enough for validation purpose. Considering a reference area of 13.5 × 13.5 m2, we quantify temporal stable and small uncertainties for the spectral range between 720 and 800 nm. For an all-embracing validation of the surface reflectance of vegetated areas, the chosen site is not appropriate.

Urban Roadway in America: The Amount, Extent, and Value

Problem, research strategy, and findings We predicted the amount, share, and value of land dedicated to roadways within and across 316 U.S. primary metropolitan statistical areas. Despite the amount and value of land dedicated to roadways, our study provides the first such estimate across a broad range of metropolitan areas. Our basic approach was to estimate roadway widths using a 10% sample of widths provided by the Highway Performance Monitoring System and apply our estimates to the rest of the roadway system. Multiplying estimated widths by segment length and netting out double counting at intersections provided estimates of land area. We also matched roadway segments and areas to existing land value estimates and satellite-based measures of urbanized land. We found that a little less than a quarter of urbanized land—roughly the size of West Virginia—was dedicated to roadway. This land was worth around $4.1 trillion in 2016 and had an annualized value that was higher than the total variable costs of the trucking sector and the total annual federal, state, and local expenditures on roadways. Conducting a back-of-the-envelope cost–benefit analysis, we found that the country likely has too much land dedicated to urban roads. Takeaway for practice Federal, state, and local agencies dedicate substantial time, money, and resources to providing roadways. Even with relatively generous assumptions and no external costs from driving, however, we estimated that the average cost of expanding roadways exceeded the benefits by a factor of nearly three when accounting for land value. Policymakers should question policies focused on roadway expansion and consider options to reduce the amount of space dedicated to roadway in favor of more housing, offices, and other land uses. In addition to our findings, we provide a novel data set that academics and policymakers can use to draw their own conclusions about the state of America’s urban roadways.

Ground Urban Heat Island: Strengthening the Connection Between Spaceborne Thermal Observations and Urban Heat Risk Management.

As urbanization progresses under a changing climate, urban populations face increasing threats from chronically higher heat exposures and more frequent extreme heat events. Understanding the complex urban thermal exposure patterns becomes crucial for effective heat risk management. The spatial advantage of satellite thermal observations positions surface urban heat islands (SUHI) as a primary measure for such applications at the city scale. However, satellite-inherent biases pose considerable uncertainties. To improve the representation of human-relevant heat exposure, this study proposes a simple but effective satellite-based measure- ground urban heat island (GUHI), focusing solely on radiant temperatures from urban ground elements. Leveraging ECOSTRESS land surface temperature product and radiation-based statistical downscaling, diurnally representative GUHIs were evaluated over NYC. The findings reveal that overall GUHI is consistently warmer than SUHI diurnally. However, GUHI exhibits complex spatial contrasts with SUHI, primarily influenced by vegetation coverage. Various indicators associated with urban structures and materials were examined, showing important but dissimilar roles in shaping the spatial dynamics of GUHI and SUHI. This study highlights the value of satellite thermal observations compared to air temperature while addressing uncertainties in widely adopted practices of using them. By improving the depiction of human-related urban heat patterns from Earth observations, this research offers valuable insight and more reliable measures to address the urgent requirements for urban heat risk management globally.

Satellite-based water surface slope over a small mountain river in northern China

Satellite altimetry has become a crucial alternative for measuring inland water surface elevations apart from ground observations. Water surface slope (WSS), a fundamental parameter in river discharge simulation, also allowing for assimilating satellite and ground-observed water level data with different observation positions. There are various satellite data products offering precise measurements of river water levels and estimates of WSS on a global scale. However, satellites observation of water surface over small rivers, particularly those in mountainous areas with narrow river channels, continues to be difficult. This study focuses on evaluating the precision of the ICESat-2 ATL03 photon height data in estimating WSS throughout the mountainous river section of Yongding River, which flows across the Hebei Province and Beijing City in northern China. The ICESat-2 ATL03 data with minimum along-track sampling interval of 0.7 m makes it possible to accurately estimate the WSS for small river sections that ranged from 50 to 100 m in width. A histogram-based statistical approach was used for identifying the satellite lidar photon height located right over the water surface. The eleven pairs of satellite virtual stations selected for extracting river water surface elevation and estimating river WSS demonstrated an overall validation correlation coefficient of 0.98. The range of relative error in WSS estimation is between 0.13 % and 9.02 %. The results of this work have important implications for using satellite-based measurements to estimate critical hydrological parameters in small and mountainous river basins in the absence of ground observations.

Atlas of phytoplankton phenology indices in selected Eastern Mediterranean marine ecosystems.

Phytoplankton is a fundamental component of marine food webs and play a crucial role in marine ecosystem functioning. The phenology (timing of growth) of these microscopic algae is an important ecological indicator that can be utilized to observe its seasonal dynamics, and assess its response to environmental perturbations. Ocean colour remote sensing is currently the only means of obtaining synoptic estimates of chlorophyll-a (a proxy of phytoplankton biomass) at high temporal and spatial resolution, enabling the calculation of phenology metrics. However, ocean colour observations have acknowledged weaknesses compromising its reliability, while the scarcity of long-term in situ data has impeded the validation of satellite-derived phenology estimates. To address this issue, we compared one of the longest available in situ time series (20 years) of chlorophyll-a concentrations in the Eastern Mediterranean Sea (EMS), along with concurrent remotely-sensed observations. The comparison revealed a marked coherence between the two datasets, indicating the capability of satellite-based measurements in accurately capturing the phytoplankton seasonality and phenology metrics (i.e., timing of initiation, duration, peak and termination) in the studied area. Furthermore, by studying and validating these metrics we constructed a satellite-derived phytoplankton phenology atlas, reporting in detail the seasonal patterns in several sub-regions in coastal and open seas over the EMS. The open waters host higher concentrations from late October to April, with maximum levels recorded during February and lowest during the summer period. The phytoplankton growth over the Northern Aegean Sea appeared to initiate at least a month later than the rest of the EMS (initiating in late November and terminating in late May). The coastal waters and enclosed gulfs (such as Amvrakikos and Maliakos), exhibit a distinct seasonal pattern with consistently higher levels of chlorophyll-a and prolonged growth period compared to the open seas. The proposed phenology atlas represents a useful resource for monitoring phytoplankton growth periods in the EMS, supporting water quality management practices, while enhancing our current comprehension on the relationships between phytoplankton biomass and higher trophic levels (as a food source).

Efficient Data-Driven Gap Filling of Satellite Image Time Series Using Deep Neural Networks with Partial Convolutions

Abstract The abundance of gaps in satellite image time series often complicates the application of deep learning models such as convolutional neural networks for spatiotemporal modeling. Based on previous work in computer vision on image inpainting, this paper shows how three-dimensional spatiotemporal partial convolutions can be used as layers in neural networks to fill gaps in satellite image time series. To evaluate the approach, we apply a U-Net-like model on incomplete image time series of quasi-global carbon monoxide observations from the Sentinel-5 Precursor (Sentinel-5P) satellite. Prediction errors were comparable to two considered statistical approaches while computation times for predictions were up to three orders of magnitude faster, making the approach applicable to process large amounts of satellite data. Partial convolutions can be added as layers to other types of neural networks, making it relatively easy to integrate with existing deep learning models. However, the approach does not provide prediction uncertainties and further research is needed to understand and improve model transferability. The implementation of spatiotemporal partial convolutions and the U-Net-like model is available as open-source software. Significance Statement Gaps in satellite-based measurements of atmospheric variables can make the application of complex analysis methods such as deep learning approaches difficult. The purpose of this study is to present and evaluate a purely data-driven method to fill incomplete satellite image time series. The application on atmospheric carbon monoxide data suggests that the method can achieve prediction errors comparable to other approaches with much lower computation times. Results highlight that the method is promising for larger datasets but also that care must be taken to avoid extrapolation. Future studies may integrate the approach into more complex deep learning models for understanding spatiotemporal dynamics from incomplete data.

Investigation of ground movements induced by underground gas storages via unsupervised ML methodology applied to InSAR data

The research explores the definition, formalization, and validation of a sound and rigorous methodology for analyzing a vast amount of satellite-based measures to geo-localize and quantify the ground movements induced by the storage of natural gas in underground formations. Time series decomposition analysis and unsupervised machine learning algorithms (partitive and hierarchical clustering) are adopted for processing, categorizing, and interpreting ground vertical movements from InSAR acquisitions. At the surface level, storage operations induce characteristic seasonal and cyclical movements, showing uplift during the injection period and subsidence during the withdrawal one. Consequently, the analysis of the solely sinusoidal component of the vertical movements (obtained via the time-series decomposition) turns out to be the key aspect of the proposed approach for handling the superposition of different ground movement sources, and consequently for clearly and reliably identifying the effects of underground gas storage (UGS) only.The proposed methodology was validated using two independent case studies in the Po Plain (northern Italy), a highly urbanized area affected by ground movements induced by several natural and anthropogenic causes, including underground gas storage facilities. For each case study, the methodology localizes one well-defined and confined area as the most affected by storage operations: this area corresponds to a cluster characterized by a high cohesion and by a seasonality phase coherent with the storage injection/withdrawal periods. The other clusters group areally wide-spread measurement points; the phase of their sinusoidal curves shows no time-coherency (or even phase opposition) with the seasonal storage operations. The results were verified via available independent information about the storage locations and were compared with the findings of previous research.

Abstract MP76: Street View Greenspace Exposures and Cardiovascular Health Among Children - Evidence From Project Viva

Introduction: Research on greenspace and cardiovascular health (CVH) in children is limited, and most previous studies rely on coarse satellite-based measurements of greenspace, which do not capture exposure as individuals experience it. Street View imagery (SVI) enables measurement of greenspace from a ground-level perspective and captures specific elements of greenspace that may be most relevant to health. This study aimed to examine associations of SVI-based greenspace with CVH in children in Project Viva, an eastern Massachusetts-based cohort. Methods: Using data from 1,163 children, we applied deep learning segmentation algorithms to SVI from 2007-2010 to derive metrics of greenspace (%of trees, or grass) within a 500m radius of participants’ residential addresses obtained during mid-childhood (mean age 7.9y). At mid-childhood and early adolescence (mean age 13.2y), we calculated CVH scores (range 0-100 units; higher scores represent better CVH) using the American Heart Association’s Life’s Essential 8 algorithm, based on 4 biomedical measures (body mass index, blood lipids, blood glucose, and blood pressure) and 4 behavioral measures (heart healthy levels of diet, physical activity, avoidance of nicotine, and sleep). Using linear regression, we examined associations of mid-childhood SVI-based greenspace metrics with mid-childhood (cross-sectional) and early adolescence (longitudinal) CVH scores, adjusting for child’s sex, race and ethnicity, and age at outcome, as well as an index score of socioeconomic status comprising individual and neighborhood metrics. Results: Among the 1163 participants, 65% were non-Hispanic White and 49% were female. The mean (SD) LE8 score was 83.3 (8.0) in mid-childhood and 81.7 (8.9) in early adolescence. In the unadjusted models, a higher %trees and grass per SD, as measured by SVI within children’s residence in mid-childhood, was associated with a higher overall CVH score (β 1.63 [95%CI: 0.93, 2.33] for trees; β 1.10 [95%CI: 0.36, 1.83] for grass). In longitudinal analyses, the results were similar (β 1.69 [95%CI: 0.87, 2.52] for trees; β 0.82 [95%CI: -0.01, 1.64] for grass). In fully-adjusted analyses, the cross-sectional and longitudinal results for %trees and grass were attenuated and not statistically significant. However, when CVH scores were disaggregated by behavioral and biomedical sub-scores, %trees was positively associated with behavioral sub-scores in cross-sectional fully-adjusted analyses at mid-childhood (β 0.89 [95%CI: 0.32, 1.46]). Conclusion: Among participants in Project Viva, SVI-based metrics of greenspace exposure in mid-childhood were cross-sectionally associated with behavioral factors promoting higher CVH. Greenspace may hold promise for improving children’s CVH through the behavioral pathway and should be considered in future health-promotion efforts.

A review of the Indian Ocean carbon dynamics, acidity, and productivity in a changing environment

The Indian Ocean dynamics is governed by the seasonal reversal of monsoon winds and the associated ocean currents. The relatively deep thermocline along the equator due to a lack of steady easterlies, low-latitude connection to the neighbouring Pacific, and a lack of northward heat export due to the position of the Asian continent are important factors in regulating the ocean state. These features make it a unique ecosystem among the world's tropical oceans and determine key features of potential air-sea interaction at different time scales. The pCO2 shows a large seasonal variation linked with monsoon circulation. The Indian Ocean’s northwestern part acts as an atmospheric CO2 source, whereas the northeastern part acts as a net atmospheric CO2 sink. The region between the latitudes of 15°S-50°S in the Indian Ocean is a major subduction zone because of positive wind stress curl. The subducted water masses are transported to the northern Indian Ocean by the cross-equatorial cell (a shallow meridional overturning circulation). Based on the regional studies carried out on the carbonate system in the Indian Ocean, the area north of 15°S is a source of atmospheric CO2, while the area between 15°S and 50°S is a sink. A recent synthesis of models (observational climatology) over different spatial scales provides an estimate of the mean value of CO2 in the north of 37.5°S of the Indian Ocean as − 0.19 ± 0.1 PgC/yr (−0.07 ± 0.14 PgC/yr) during 1985–2018. The estimated decrease in pH (acidification) using model outputs in the Indian Ocean basin is 0.0675 units during 1961–2010, in which the contribution of dissolved inorganic carbon and surface temperature is 69.3 % and 13.8 %, respectively. The range of the Indian Ocean’s annual primary production based on satellite estimates over the last two decades (1998–2018) is 7.72–8.70 Gt C/yr, whereas the climatological mean is 8.24 ± 0.30 Gt C/yr. This paper consolidates the current state of understanding of the Indian Ocean carbon fluxes, acidification, and productivity using available field and satellite-based measurements, model simulations, and re-constructed datasets. It provides an overview of the functioning of the Indian Ocean’s air-to-sea CO2 exchange and highlights its influence on global climate. Finally, it aims to highlight the grey areas of the Indian Ocean carbon cycle that need to be understood.

Drivers and impacts of decreasing concentrations of atmospheric volatile organic compounds (VOCs) in Beijing during 2016–2020

China has implemented various policies and measures for controlling air pollutants. However, our knowledge of the long-term trends in ambient volatile organic compounds (VOCs) after the implementation of these action plans in China remains limited. To address this, we conducted a five-year analysis (2016–2020) of VOC compositions and concentrations in Beijing. The annual VOC concentration decreased from 44.0 ± 28.8 to 26.2 ± 16.4 ppbv, with alkanes being the most prevalent group. The annual average concentrations of alkenes, alkynes, and aromatics have experienced a significant decrease of over 50 %. Seasonal variations indicated higher VOC concentrations in winter and autumn, with more significant reductions observed in winter and autumn. The impact of meteorological conditions caused variations in VOC reductions during the Chinese Spring Festival. Satellite-based measurements of formaldehyde (HCHO) columns confirmed the reduction of VOC emissions during the Coronavirus (COVID-19) lockdown. The normalized annual average VOC concentration decreased by 2.9ppbv yr−1 from 2016 to 2020, and emission reduction contributed to 58.8 % of VOC reduction from 2016 to 2020 after meteorological normalization, indicating the effectiveness of implemented control measures. Based on receptor model, vehicle emissions and industrial sources were identified as the largest contributors to VOC concentrations. Vehicle emissions, liquefied petroleum gas/natural gas (LPG/NG) use, and coal combustion were major drivers of VOC reduction. Potential source region analysis revealed that air masses transported from northwestern and southern regions significantly contributed to VOC concentrations in Beijing. The range of source regions shrunk in both northwestern and southern regions with the reduction in VOC concentrations. The annual variations of ozone formation potential indicated a significant decrease in VOC reactivities through emission control. These results could provide insights into future emission control and coordinated efforts to improve PM2.5 and ozone levels in China.

Evaluating the Ability of the Pre-Launch TanSat-2 Satellite to Quantify Urban CO2 Emissions

TanSat-2, the next-generation Chinese greenhouse gas monitoring satellite for measuring carbon dioxide (CO2), has a new city-scale observing mode. We assess the theoretical capability of TanSat-2 to quantify integrated urban CO2 emissions over the cities of Beijing, Jinan, Los Angeles, and Paris. A high-resolution emission inventory and a column-averaged CO2 (XCO2) transport model are used to build an urban CO2 inversion system. We design a series of numerical experiments describing this observing system to evaluate the impacts of sampling patterns and XCO2 measurement errors on inferring urban CO2 emissions. We find that the correction in systematic and random flux errors is correlated with the signal-to-noise ratio of satellite measurements. The reduction in systematic flux errors for the four cities are sizable, but are subject to unbiased satellite sampling and favorable meteorological conditions (i.e., less cloud cover and lower wind speed). The corresponding correction to the random flux error is 19–28%. Even though clear-sky satellite data from TanSat-2 have the potential to reduce flux errors for cities with high CO2 emissions, quantifying urban emissions by satellite-based measurements is subject to additional limitations and uncertainties.

Integrating micro-air quality sensors and satellite-based measurements to establish a high spatial-temporal resolution PM2.5 model in Taiwan

Integrating micro-air quality sensors and satellite-based measurements to establish a high spatial-temporal resolution PM2.5 model in Taiwan

The Economics of Tropical Deforestation

Two factors have elevated recent academic and policy interest in tropical deforestation: first, the realization that it is a major contributor to climate change; and second, a revolution in satellite-based measurement that has revealed that it is proceeding at a rapid rate. We begin by reviewing the methodological advances that have enabled measurement of forest loss at a fine spatial resolution across the globe. We then develop a simple benchmark model of deforestation based on classic models of natural resource extraction. Extending this approach to incorporate features that characterize deforestation in developing countries—pressure for land use change, significant local and global externalities, weak property rights, and political economy constraints—provides us with a framework for reviewing the fast-growing empirical literature on the economics of deforestation in the tropics. This combination of theory and empirics provides insights not only into the economic drivers and impacts of tropical deforestation but also into policies that may affect its progression. We conclude by identifying areas where more work is needed in this important body of research.

Air pollution and suicide in rural and urban America: Evidence from wildfire smoke

Air pollution poses well-established risks to physical health, but little is known about its effects on mental health. We study the relationship between wildfire smoke exposure and suicide risk in the United States in 2007 to 2019 using data on all deaths by suicide and satellite-based measures of wildfire smoke and ambient fine particulate matter (PM2.5) concentrations. We identify the causal effects of wildfire smoke pollution on suicide by relating year-over-year fluctuations in county-level monthly smoke exposure to fluctuations in suicide rates and compare the effects across local areas and demographic groups that differ considerably in their baseline suicide risk. In rural counties, an additional day of smoke increases monthly mean PM2.5 by 0.41 μg/m3 and suicide deaths by 0.11 per million residents, such that a 1-μg/m3 (13%) increase in monthly wildfire-derived fine particulate matter leads to 0.27 additional suicide deaths per million residents (a 2.0% increase). These effects are concentrated among demographic groups with both high baseline suicide risk and high exposure to outdoor air: men, working-age adults, non-Hispanic Whites, and adults with no college education. By contrast, we find no evidence that smoke pollution increases suicide risk among any urban demographic group. This study provides large-scale evidence that air pollution elevates the risk of suicide, disproportionately so among rural populations.

Revisiting the effects of the Ethiopian land tenure reform using satellite data. A focus on agricultural productivity, climate change mitigation and adaptation

This study examines the effects of the land registration and certification programme introduced in 1998 in the Tigray region of Ethiopia on agricultural productivity, climate change mitigation and adaptation. We use satellite-based measures of greenness and implement a difference-in-differences approach, comparing pixels on both sides of the Tigray-Amhara regional border. Results show positive and persistent effects of the programme on agricultural productivity and climate change mitigation. By examining years when adverse climate and weather events occurred, we also find evidence of increased adaptation to climate change. We show that our results are consistent with the reform enhancing farmers’ tenure security and inducing an increase in the adoption of climate smart agricultural practices.

Critical Review on Radiative Forcing and Climate Models for Global Climate Change since 1970

This review identifies a critical problem in the fundamental physics of current climate models. The large greenhouse effect of rising CO2 assumed in climate models is assessed by six key observations from ground- and satellite-based measurements. This assessment is enhanced by statistical analyses and model calculations of global or regional mean surface temperature changes by conventional climate models and by a conceptual quantum physical model of global warming due to halogen-containing greenhouse gases (halo-GHGs). The postulated large radiative forcing of CO2 in conventional climate models does not agree with satellite observations. Satellite-observed warming pattern resembles closely the atmospheric distribution of chlorofluorocarbons (CFCs). This review helps understand recent remarkable observations of reversals from cooling to warming in the lower stratosphere over most continents and in the upper stratosphere at high latitudes, surface warming cessations in the Antarctic, North America, UK, and Northern-Hemisphere (NH) extratropics, and the stabilization in NH or North America snow cover, since the turn of the century. The complementary observation of surface temperature changes in 3 representative regions (Central England, the Antarctic, and the Arctic) sheds new light on the primary mechanism of global warming. These observations agree well with not CO2-based climate models but the CFC-warming quantum physical model. The latter offers parameter-free analytical calculations of surface temperature changes, exhibiting remarkable agreement with observations. These observations overwhelmingly support an emerging picture that halo-GHGs made the dominant contribution to global warming in the late 20th century and that a gradual reversal in warming has occurred since ~2005 due to the phasing out of halo-GHGs. Advances and insights from this review may help humans make rational policies to reverse the past warming and maintain a healthy economy and ecosystem.

A hybrid model for estimating the number concentration of ultrafine particles based on machine learning algorithms in central Taiwan

Modeling is a cost-effective measure to estimate ultrafine particle (UFP) levels. Previous UFP estimates generally relied on land-use regression with insufficient temporal resolution. We carried out in-situ measurements for UFP in central Taiwan and developed a model incorporating satellite-based measurements, meteorological variables, and land-use data to estimate daily UFP levels at a 1-km resolution. Two sampling campaigns were conducted for measuring hourly UFP concentrations at six sites between 2008–2010 and 2017–2021, respectively, using scanning mobility particle sizers. Three machine learning algorithms, namely random forest, eXtreme gradient boosting (XGBoost), and deep neural network, were used to develop UFP estimation models. The performances were evaluated with a 10-fold cross-validation, temporal, and spatial validation. A total of 1,022 effective sampling days were conducted. The XGBoost model had the best performance with a training coefficient of determination (R2) of 0.99 [normalized root mean square error (nRMSE): 6.52%] and a cross-validation R2 of 0.78 (nRMSE: 31.0%). The ten most important variables were surface pressure, distance to the nearest road, temperature, calendar year, day of the year, NO2, meridional wind, the total length of roads, PM2.5, and zonal wind. The UFP levels were elevated along the main roads across different seasons, suggesting that traffic emission is an important contributor to UFP. This hybrid model outperformed prior land use regression models and thus can provide more accurate estimates of UFP for epidemiological studies.