Dynamic Traffic Data in Machine-Learning Air Quality Mapping Improves Environmental Justice Assessment.

Air pollution poses a critical public health threat around many megacities but in an uneven manner. Conventional models are limited to depict the highly spatial- and time-varying patterns of ambient pollutant exposures at the community scale for megacities. Here, we developed a machine-learning approach that leverages the dynamic traffic profiles to continuously estimate community-level year-long air pollutant concentrations in Los Angeles, U.S. We found the introduction of real-world dynamic traffic data significantly improved the spatial fidelity of nitrogen dioxide (NO2), maximum daily 8-h average ozone (MDA8 O3), and fine particulate matter (PM2.5) simulations by 47%, 4%, and 15%, respectively. We successfully captured PM2.5 levels exceeding limits due to heavy traffic activities and providing an "out-of-limit map" tool to identify exposure disparities within highly polluted communities. In contrast, the model without real-world dynamic traffic data lacks the ability to capture the traffic-induced exposure disparities and significantly underestimate residents' exposure to PM2.5. The underestimations are more severe for disadvantaged communities such as black and low-income groups, showing the significance of incorporating real-time traffic data in exposure disparity assessment.

The weekly cycle of photosynthesis in Europe reveals the negative impact of particulate pollution on ecosystem productivity.

Aerosols can affect photosynthesis through radiative perturbations such as scattering and absorbing solar radiation. This biophysical impact has been widely studied using field measurements, but the sign and magnitude at continental scales remain uncertain. Solar-induced fluorescence (SIF), emitted by chlorophyll, strongly correlates with photosynthesis. With recent advancements in Earth observation satellites, we leverage SIF observations from the Tropospheric Monitoring Instrument (TROPOMI) with unprecedented spatial resolution and near-daily global coverage, to investigate the impact of aerosols on photosynthesis. Our analysis reveals that on weekends when there is more plant-available sunlight due to less particulate pollution, 64% of regions across Europe show increased SIF, indicating more photosynthesis. Moreover, we find a widespread negative relationship between SIF and aerosol loading across Europe. This suggests the possible reduction in photosynthesis as aerosol levels increase, particularly in ecosystems limited by light availability. By considering two plausible scenarios of improved air quality-reducing aerosol levels to the weekly minimum 3-d values and levels observed during the COVID-19 period-we estimate a potential of 41 to 50 Mt net additional annual CO2 uptake by terrestrial ecosystems in Europe. This work assesses human impacts on photosynthesis via aerosol pollution at continental scales using satellite observations. Our results highlight i) the use of spatiotemporal variations in satellite SIF to estimate the human impacts on photosynthesis and ii) the potential of reducing particulate pollution to enhance ecosystem productivity.

Elucidating climatic drivers of photosynthesis by tropical forests.

Tropical forests play a pivotal role in regulating the global carbon cycle. However, the response of these forests to changes in absorbed solar energy and water supply under the changing climate is highly uncertain. Three-year (2018-2021) spaceborne high-resolution measurements of solar-induced chlorophyll fluorescence (SIF) from the TROPOspheric Monitoring Instrument (TROPOMI) provide a new opportunity to study the response of gross primary production (GPP) and more broadly tropical forest carbon dynamics to differences in climate. SIF has been shown to be a good proxy for GPP on monthly and regional scales. Combining tropical climate reanalysis records and other contemporary satellite products, we find that on the seasonal timescale, the dependence of GPP on climate variables is highly heterogeneous. Following the principal component analyses and correlation comparisons, two regimes are identified: water limited and energy limited. GPP variations over tropical Africa are more correlated with water-related factors such as vapor pressure deficit (VPD) and soil moisture, while in tropical Southeast Asia, GPP is more correlated with energy-related factors such as photosynthetically active radiation (PAR) and surface temperature. Amazonia is itself heterogeneous: with an energy-limited regime in the north and water-limited regime in the south. The correlations of GPP with climate variables are supported by other observation-based products, such as Orbiting Carbon Observatory-2 (OCO2) SIF and FluxSat GPP. In each tropical continent, the coupling between SIF and VPD increases with the mean VPD. Even on the interannual timescale, the correlation of GPP with VPD is still discernable, but the sensitivity is smaller than the intra-annual correlation. By and large, the dynamic global vegetation models in the TRENDY v8 project do not capture the high GPP seasonal sensitivity to VPD in dry tropics. The complex interactions between carbon and water cycles in the tropics illustrated in this study and the poor representation of this coupling in the current suite of vegetation models suggest that projections of future changes in carbon dynamics based on these models may not be robust.

Solutions to agricultural green water scarcity under climate change.

Rain-fed agricultural systems, which solely depend on green water (i.e. soil moisture from rainfall), sustain ∼60% of global food production and are particularly vulnerable to vagaries in temperature and precipitation patterns, which are intensifying due to climate change. Here, using projections of crop water demand and green water availability under warming scenarios, we assess global agricultural green water scarcity-defined when the rainfall regime is unable to meet crop water requirements. With present-day climate conditions, food production for 890 million people is lost because of green water scarcity. Under 1.5°C and 3°C warming-the global warming projected from the current climate targets and business as usual policies-green water scarcity will affect global crop production for 1.23 and 1.45 billion people, respectively. If adaptation strategies were to be adopted to retain more green water in the soil and reduce evaporation, we find that food production loss from green water scarcity would decrease to 780 million people. Our results show that appropriate green water management strategies have the potential to adapt agriculture to green water scarcity and promote global food security.

Unequal exposure to heatwaves in Los Angeles: Impact of uneven green spaces.

Cities worldwide are experiencing record-breaking summer temperatures. Urban environments exacerbate extreme heat, resulting in not only the urban heat island but also intracity variations in heat exposure. Understanding these disparities is crucial to support equitable climate mitigation and adaptation efforts. We found persistent negative correlations between daytime land surface temperature (LST) and median household income across the Los Angeles metropolitan area based on Ecosystem Spaceborne Thermal Radiometer Experiment on Space Station observations from 2018 to 2021. Lower evapotranspiration resulting from the unequal distribution of vegetation cover is a major factor leading to higher LST in low-income neighborhoods. Disparities worsen with higher regional mean surface temperature, with a $10,000 decrease in income leading to ~0.2°C LST increase at 20°C and up to ~0.7°C at 45°C. With more frequent and intense heat waves projected in the future, equitable mitigation measures, such as increasing surface albedo and tree cover in low-income neighborhoods, are necessary to address these disparities.

Comment on "Recent global decline of CO2 fertilization effects on vegetation photosynthesis".

Wang et al. (Research Articles, 11 December 2020, p. 1295), using photosynthesis proxies from long-term satellite records, report a significant decline in CO2 fertilization effects. We find that small systematic biases in Advanced Very High Resolution Radiometer (AVHRR) data affect their analysis to the degree that the key finding is not robust. Caution is recommended when using AVHRR to detect changes in near-infrared reflectance of vegetation (NIRv) trends and vegetation indices.

Effect of Low-Dose Aspirin on Midluteal Phase Uterine Artery Blood Flow in Patients With Recurrent Pregnancy Loss.

OBJECTIVES: The purpose of this study was to evaluate differences in uterine artery blood flow parameters and pregnancy outcomes, if any. An investigation was conducted to determine the effects of low-dose aspirin on uterine artery blood flow indices in patients with recurrent pregnancy loss. METHODS: This observational study included 353 Chinese women with a history of recurrent pregnancy loss and 85 women without a history of recurrent pregnancy loss (control group) from Ren Ji Hospital. All patients were scanned transvaginally with transvaginal Doppler sonography 6 to 8 days after ovulation to measure the pulsatility index (PI), resistive index (RI), and systolic-to-diastolic ratio (S/D) of the left and right main uterine arteries. Low-dose aspirin at a dose of 50 mg/d was administered orally in patients with recurrent pregnancy loss for 2 months, and the blood flow indices were measured subsequently. The Student t test was used for analysis of the results, P < .05 was considered significant. RESULTS: The mean PI and S/D of the uterine arteries in the recurrent pregnancy loss group were significantly higher than in the control group. Although not statistically significant, the RI was higher in the recurrent pregnancy loss group than the control group. Moreover, the PI and S/D increased as the number of pregnancy losses increased. Significant enhancements of the PI and S/D were observed in patients with 4 or more consecutive abortions. After low-dose aspirin supplementation, patients with recurrent pregnancy loss showed a highly significant reduction in the PI and S/D. CONCLUSIONS: Uterine blood flow decreased during the luteal phase in patients with recurrent pregnancy loss. Low-dose aspirin induced a reversible increase in uterine blood flow and may be of therapeutic value.