Increased heat risk in wet climate induced by urban humid heat.

Cities are generally warmer than their adjacent rural land, a phenomenon known as the urban heat island (UHI). Often accompanying the UHI effect is another phenomenon called the urban dry island (UDI), whereby the humidity of urban land is lower than that of the surrounding rural land1-3. The UHI exacerbates heat stress on urban residents4,5, whereas the UDI may instead provide relief because the human body can cope with hot conditions better at lower humidity through perspiration6,7. The relative balance between the UHI and the UDI-as measured by changes in the wet-bulb temperature (Tw)-is a key yet largely unknown determinant of human heat stress in urban climates. Here we show that Tw is reduced in cities in dry and moderately wet climates, where the UDI more than offsets the UHI, but increased in wet climates (summer precipitation of more than 570 millimetres). Our results arise from analysis of urban and rural weather station data across the world and calculations with an urban climate model. In wet climates, the urban daytime Tw is 0.17 ± 0.14 degrees Celsius (mean ± 1 standard deviation) higher than rural Tw in the summer, primarily because of a weaker dynamic mixing in urban air. This Tw increment is small, but because of the high background Tw in wet climates, it is enough to cause two to six extra dangerous heat-stress days per summer for urban residents under current climate conditions. The risk of extreme humid heat is projected to increase in the future, and these urban effects may further amplify the risk.

Life-Course Health Risk Assessment of PM2.5 Elements in China: Exposure Disparities by Species, Source, Age, Gender, and Location.

Key stages in people's lives have particular relevance for their health; the life-course approach stresses the importance of these stages. Here, we applied a life-course approach to analyze the health risks associated with PM2.5-bound elements, which were measured at three sites with varying environmental conditions in eastern China. Road traffic was found to be the primary source of PM2.5-bound elements at all three locations, but coal combustion was identified as the most important factor to induce both cancer risk (CR) and noncancer risk (NCR) across all age groups due to the higher toxicity of elements such as As and Pb associated with coal. Nearly half of NCR and over 90% of CR occurred in childhood (1-6 years) and adulthood (>18 years), respectively, and females have slightly higher NCR and lower CR than males. Rural population is found to be subject to the highest health risks. Synthesizing previous relevant studies and nationwide PM2.5 concentration measurements, we reveal ubiquitous and large urban-rural environmental exposure disparities over China.

Low-Cost CO2 NDIR Sensors: Performance Evaluation and Calibration Using Machine Learning Techniques.

The study comprehensively evaluates low-cost CO2 sensors from different price tiers, assessing their performance against a reference-grade instrument and exploring the possibility of calibration using different machine learning techniques. Three sensors (Sunrise AB by Senseair, K30 CO2 by Senseair, and GMP 343 by Vaisala) were tested alongside a reference instrument (Los Gatos precision greenhouse gas analyzer). The results revealed differences in sensor performance, with the higher cost Vaisala sensors exhibiting superior accuracy. Despite its lower price, the Sunrise sensors still demonstrated reasonable accuracy. Meanwhile, the K30 sensor measurements displayed higher variability and noise. Machine learning models, including linear regression, gradient boosting regression, and random forest regression, were employed for sensor calibration. In general, linear regression models performed best for extrapolating data, whereas decision tree-based models were generally more useful in handling non-linear datasets. Notably, a stack ensemble model combining these techniques outperformed the individual models and significantly improved sensor accuracy by approximately 65%. Overall, this study contributes to filling the gap in intercomparing CO2 sensors across different price categories and underscores the potential of machine learning for enhancing sensor accuracy, particularly in low-cost sensor applications.

Quantification of Diffusive Methane Emissions from a Large Eutrophic Lake with Satellite Imagery.

Lakes are major emitters of methane (CH4); however, a longstanding challenge with quantifying the magnitude of emissions remains as a result of large spatial and temporal variability. This study was designed to address the issue using satellite remote sensing with the advantages of spatial coverage and temporal resolution. Using Aqua/MODIS imagery (2003-2020) and in situ measured data (2011-2017) in eutrophic Lake Taihu, we compared the performance of eight machine learning models to predict diffusive CH4 emissions and found that the random forest (RF) model achieved the best fitting accuracy (R2 = 0.65 and mean relative error = 21%). On the basis of input satellite variables (chlorophyll a, water surface temperature, diffuse attenuation coefficient, and photosynthetically active radiation), we assessed how and why they help predict the CH4 emissions with the RF model. Overall, these variables mechanistically controlled the emissions, leading to the model capturing well the variability of diffusive CH4 emissions from the lake. Additionally, we found climate warming and associated algal blooms boosted the long-term increase in the emissions via reconstructing historical (2003-2020) daily time series of CH4 emissions. This study demonstrates the great potential of satellites to map lake CH4 emissions by providing spatiotemporal continuous data, with new and timely insights into accurately understanding the magnitude of aquatic greenhouse gas emissions.

Urban warming advances spring phenology but reduces the response of phenology to temperature in the conterminous United States.

Urbanization has caused environmental changes, such as urban heat islands (UHIs), that affect terrestrial ecosystems. However, how and to what extent urbanization affects plant phenology remains relatively unexplored. Here, we investigated the changes in the satellite-derived start of season (SOS) and the covariation between SOS and temperature (RT ) in 85 large cities across the conterminous United States for the period 2001-2014. We found that 1) the SOS came significantly earlier (6.1 ± 6.3 d) in 74 cities and RT was significantly weaker (0.03 ± 0.07) in 43 cities when compared with their surrounding rural areas (P < 0.05); 2) the decreased magnitude in RT mainly occurred in cities in relatively cold regions with an annual mean temperature <17.3 °C (e.g., Minnesota, Michigan, and Pennsylvania); and 3) the magnitude of urban-rural difference in both SOS and RT was primarily correlated with the intensity of UHI. Simulations of two phenology models further suggested that more and faster heat accumulation contributed to the earlier SOS, while a decrease in required chilling led to a decline in RT magnitude in urban areas. These findings provide observational evidence of a reduced covariation between temperature and SOS in major US cities, implying the response of spring phenology to warming conditions in nonurban environments may decline in the warming future.

Development and Performance Evaluation of a Low-Cost Portable PM2.5 Monitor for Mobile Deployment.

The concentration of fine particulate matter (PM2.5) is known to vary spatially across a city landscape. Current networks of regulatory air quality monitoring are too sparse to capture these intra-city variations. In this study, we developed a low-cost (60 USD) portable PM2.5 monitor called Smart-P, for use on bicycles, with the goal of mapping street-level variations in PM2.5 concentration. The Smart-P is compact in size (85 × 85 × 42 mm) and light in weight (147 g). Data communication and geolocation are achieved with the cyclist's smartphone with the help of a user-friendly app. Good agreement was observed between the Smart-P monitors and a regulatory-grade monitor (mean bias error: −3.0 to 1.5 µg m−3 for the four monitors tested) in ambient conditions with relative humidity ranging from 38 to 100%. Monitor performance decreased in humidity > 70% condition. The measurement precision, represented as coefficient of variation, was 6 to 9% in stationary mode and 6% in biking mode across the four tested monitors. Street tests in a city with low background PM2.5 concentrations (8 to 9 µg m−3) and in two cities with high background concentrations (41 to 74 µg m−3) showed that the Smart-P was capable of observing local emission hotspots and that its measurement was not sensitive to bicycle speed. The low-cost and user-friendly nature are two features that make the Smart-P a good choice for empowering citizen scientists to participate in local air quality monitoring.

Biases in open-path carbon dioxide flux measurements: Roles of instrument surface heat exchange and analyzer temperature sensitivity.

Eddy covariance (EC) measurements of ecosystem-atmosphere carbon dioxide (CO2) exchange provide the most direct assessment of the terrestrial carbon cycle. Measurement biases for open-path (OP) CO2 concentration and flux measurements have been reported for over 30 years, but their origin and appropriate correction approach remain unresolved. Here, we quantify the impacts of OP biases on carbon and radiative forcing budgets for a sub-boreal wetland. Comparison with a reference closed-path (CP) system indicates that a systematic OP flux bias (0.54 µmol m-2 s-1) persists for all seasons leading to a 110% overestimate of the ecosystem CO2 sink (cumulative error of 78 gC m-2). Two potential OP bias sources are considered: Sensor-path heat exchange (SPHE) and analyzer temperature sensitivity. We examined potential OP correction approaches including: i) Fast temperature measurements within the measurement path and sensor surfaces; ii) Previously published parameterizations; and iii) Optimization algorithms. The measurements revealed year-round average temperature and heat flux gradients of 2.9 °C and 16 W m-2 between the bottom sensor surfaces and atmosphere, indicating SPHE-induced OP bias. However, measured SPHE correlated poorly with the observed differences between OP and CP CO2 fluxes. While previously proposed nominally universal corrections for SPHE reduced the cumulative OP bias, they led to either systematic under-correction (by 38.1 gC m-2) or to systematic over-correction (by 17-37 gC m-2). The resulting budget errors exceeded CP random uncertainty and change the sign of the overall carbon and radiative forcing budgets. Analysis of OP calibration residuals as a function of temperature revealed a sensitivity of 5 µmol m-3 K-1. This temperature sensitivity causes CO2 calibration errors proportional to sample air fluctuations that can offset the observed growing season flux bias by 50%. Consequently, we call for a new OP correction framework that characterizes SPHE- and temperature-induced CO2 measurement errors.

The fertilization effect of global dimming on crop yields is not attributed to an improved light interception.

Global dimming, a decadal decrease in incident global radiation, is often accompanied with an increase in the diffuse radiation fraction, and, therefore, the impact of global dimming on crop production is hard to predict. A popular approach to quantify this impact is the statistical analysis of historical climate and crop data, or use of dynamic crop simulation modelling approach. Here, we show that statistical analysis of historical data did not provide plausible values for the effect of diffuse radiation versus direct radiation on rice or wheat yield. In contrast, our field experimental study of 3 years demonstrated a fertilization effect of increased diffuse radiation fraction, which partly offset yield losses caused by decreased global radiation, in both crops. The fertilization effect was not attributed to any improved canopy light interception but mainly to the increased radiation use efficiency (RUE). The increased RUE was explained not only by the saturating shape of photosynthetic light response curves but also by plant acclimation to dimming that gradually increased leaf nitrogen concentration. Crop harvest index slightly decreased under dimming, thereby discounting the fertilization effect on crop yields. These results challenge existing modelling paradigms, which assume that the fertilization effect on crop yields is mainly attributed to an improved light interception. Further studies on the physiological mechanism of plant acclimation are required to better quantify the global dimming impact on agroecosystem productivity under future climate change.

Stable Sulfur Isotopes Revealed a Major Role of Transition-Metal Ion-Catalyzed SO2 Oxidation in Haze Episodes.

Secondary sulfate aerosols played an important role in aerosol formation and aging processes, especially during haze episodes in China. Secondary sulfate was formed via atmospheric oxidation of SO2 by OH, O3, H2O2, and transition-metal-catalyzed (TMI) O2. However, the relative importance of these oxidants in haze episodes was strongly debated. Here, we use stable sulfur isotopes (δ34S) of sulfate aerosols and a Rayleigh distillation model to quantify the contributions of each oxidant during a haze episode in Nanjing, a megacity in China. The observed δ34S values of sulfate aerosols showed a negative correlation with sulfur oxidation ratios, which was attributed to the sulfur isotopic fractionations during the sulfate formation processes. Using the average fractionation factor calculated from our observations and zero-dimensional (0-D) atmospheric chemistry modeling estimations, we suggest that OH oxidation was trivial during the haze episode, while the TMI pathway contributed 49 ± 10% of the total sulfate production and O3/H2O2 oxidations accounted for the rest. Our results displayed good agreement with several atmospheric chemistry models that carry aqueous and heterogeneous TMI oxidation pathways, suggesting the role of the TMI pathway was significant during haze episodes.

Strong contributions of local background climate to urban heat islands.

The urban heat island (UHI), a common phenomenon in which surface temperatures are higher in urban areas than in surrounding rural areas, represents one of the most significant human-induced changes to Earth's surface climate. Even though they are localized hotspots in the landscape, UHIs have a profound impact on the lives of urban residents, who comprise more than half of the world's population. A barrier to UHI mitigation is the lack of quantitative attribution of the various contributions to UHI intensity (expressed as the temperature difference between urban and rural areas, ΔT). A common perception is that reduction in evaporative cooling in urban land is the dominant driver of ΔT (ref. 5). Here we use a climate model to show that, for cities across North America, geographic variations in daytime ΔT are largely explained by variations in the efficiency with which urban and rural areas convect heat to the lower atmosphere. If urban areas are aerodynamically smoother than surrounding rural areas, urban heat dissipation is relatively less efficient and urban warming occurs (and vice versa). This convection effect depends on the local background climate, increasing daytime ΔT by 3.0 ± 0.3 kelvin (mean and standard error) in humid climates but decreasing ΔT by 1.5 ± 0.2 kelvin in dry climates. In the humid eastern United States, there is evidence of higher ΔT in drier years. These relationships imply that UHIs will exacerbate heatwave stress on human health in wet climates where high temperature effects are already compounded by high air humidity and in drier years when positive temperature anomalies may be reinforced by a precipitation-temperature feedback. Our results support albedo management as a viable means of reducing ΔT on large scales.

Nitrous oxide emissions are enhanced in a warmer and wetter world.

Nitrous oxide (N2O) has a global warming potential that is 300 times that of carbon dioxide on a 100-y timescale, and is of major importance for stratospheric ozone depletion. The climate sensitivity of N2O emissions is poorly known, which makes it difficult to project how changing fertilizer use and climate will impact radiative forcing and the ozone layer. Analysis of 6 y of hourly N2O mixing ratios from a very tall tower within the US Corn Belt-one of the most intensive agricultural regions of the world-combined with inverse modeling, shows large interannual variability in N2O emissions (316 Gg N2O-N⋅y-1 to 585 Gg N2O-N⋅y-1). This implies that the regional emission factor is highly sensitive to climate. In the warmest year and spring (2012) of the observational period, the emission factor was 7.5%, nearly double that of previous reports. Indirect emissions associated with runoff and leaching dominated the interannual variability of total emissions. Under current trends in climate and anthropogenic N use, we project a strong positive feedback to warmer and wetter conditions and unabated growth of regional N2O emissions that will exceed 600 Gg N2O-N⋅y-1, on average, by 2050. This increasing emission trend in the US Corn Belt may represent a harbinger of intensifying N2O emissions from other agricultural regions. Such feedbacks will pose a major challenge to the Paris Agreement, which requires large N2O emission mitigation efforts to achieve its goals.

Accumulation of Terrestrial Dissolved Organic Matter Potentially Enhances Dissolved Methane Levels in Eutrophic Lake Taihu, China.

Inland waters play an important role for the storage of chromophoric dissolved organic matter (CDOM) and outgassing of methane (CH4). However, to date, linkages between the optical dynamics of CDOM and dissolved CH4 levels remain largely unknown. We used multi-year (2012-2014) seasonal data series collected from Lake Taihu and 51 connecting channels to investigate how CDOM optical dynamics may impact dissolved CH4 levels in the lake. High dissolved CH4 in the northwestern inflowing river mouths coincided with high underwater UV-vis light availability, dissolved organic carbon (DOC), chemical oxygen demand (COD), DOM aromaticity, terrestrial humic-rich fluorescence, in situ measured terrestrial CDOM, depleted dissolved oxygen (DO), stable isotopic δ2H, and δ18O compared with other lake regions. Our results further revealed positive relationships between dissolved CH4 and CDOM absorption at 350 nm, i.e. a(350), COD, DOC, terrestrial humic-rich fluorophores, and DOM aromaticity, and negative relationships between dissolved CH4 and DO, δ2H, and δ18O. The central lake samples showed a major contribution of terrestrial-sourced molecular formulas to the ultrahigh resolution mass spectrometry data, suggesting the presence of allochthonous DOM sources even here. We conclude that an elevated terrestrial CDOM input likely enhances dissolved CH4 levels in Lake Taihu.

Indirect nitrous oxide emissions from streams within the US Corn Belt scale with stream order.

N2O is an important greenhouse gas and the primary stratospheric ozone depleting substance. Its deleterious effects on the environment have prompted appeals to regulate emissions from agriculture, which represents the primary anthropogenic source in the global N2O budget. Successful implementation of mitigation strategies requires robust bottom-up inventories that are based on emission factors (EFs), simulation models, or a combination of the two. Top-down emission estimates, based on tall-tower and aircraft observations, indicate that bottom-up inventories severely underestimate regional and continental scale N2O emissions, implying that EFs may be biased low. Here, we measured N2O emissions from streams within the US Corn Belt using a chamber-based approach and analyzed the data as a function of Strahler stream order (S). N2O fluxes from headwater streams often exceeded 29 nmol N2O-N m(-2) ⋅ s(-1) and decreased exponentially as a function of S. This relation was used to scale up riverine emissions and to assess the differences between bottom-up and top-down emission inventories at the local to regional scale. We found that the Intergovernmental Panel on Climate Change (IPCC) indirect EF for rivers (EF5r) is underestimated up to ninefold in southern Minnesota, which translates to a total tier 1 agricultural underestimation of N2O emissions by 40%. We show that accounting for zero-order streams as potential N2O hotspots can more than double the agricultural budget. Applying the same analysis to the US Corn Belt demonstrates that the IPCC EF5r underestimation explains the large differences observed between top-down and bottom-up emission estimates.

High Contribution of Nonfossil Sources to Submicrometer Organic Aerosols in Beijing, China.

Source apportionment of organic carbon (OC) and elemental carbon (EC) from PM1 (particulate matter with a diameter equal to or smaller than 1 µm) in Beijing, China was carried out using radiocarbon (14C) measurement. Despite a dominant fossil-fuel contribution to EC due to large emissions from traffic and coal combustion, nonfossil sources are dominant contributors of OC in Beijing throughout the year except during the winter. Primary emission was the most important contributor to fossil-fuel derived OC for all seasons. A clear seasonal trend was found for biomass-burning contribution to OC with the highest in autumn and spring, followed by winter and summer. 14C results were also integrated with those from positive matrix factorization (PMF) of organic aerosols from aerosol mass spectrometer (AMS) measurements during winter and spring. The results suggest that the fossil-derived primary OC was dominated by coal combustion emissions whereas secondary OC was mostly from fossil-fuel emissions. Taken together with previous 14C studies in Asia, Europe and USA, a ubiquity and dominance of nonfossil contribution to OC aerosols is identified not only in rural/background/remote regions but also in urban regions, which may be explained by cooking contributions, regional transportation or local emissions of seasonal-dependent biomass burning emission. In addition, biogenic and biomass burning derived SOA may be further enhanced by unresolved atmospheric processes.

Observed increase in local cooling effect of deforestation at higher latitudes.

Deforestation in mid- to high latitudes is hypothesized to have the potential to cool the Earth's surface by altering biophysical processes. In climate models of continental-scale land clearing, the cooling is triggered by increases in surface albedo and is reinforced by a land albedo-sea ice feedback. This feedback is crucial in the model predictions; without it other biophysical processes may overwhelm the albedo effect to generate warming instead. Ongoing land-use activities, such as land management for climate mitigation, are occurring at local scales (hectares) presumably too small to generate the feedback, and it is not known whether the intrinsic biophysical mechanism on its own can change the surface temperature in a consistent manner. Nor has the effect of deforestation on climate been demonstrated over large areas from direct observations. Here we show that surface air temperature is lower in open land than in nearby forested land. The effect is 0.85 ± 0.44 K (mean ± one standard deviation) northwards of 45° N and 0.21 ± 0.53 K southwards. Below 35° N there is weak evidence that deforestation leads to warming. Results are based on comparisons of temperature at forested eddy covariance towers in the USA and Canada and, as a proxy for small areas of cleared land, nearby surface weather stations. Night-time temperature changes unrelated to changes in surface albedo are an important contributor to the overall cooling effect. The observed latitudinal dependence is consistent with theoretical expectation of changes in energy loss from convection and radiation across latitudes in both the daytime and night-time phase of the diurnal cycle, the latter of which remains uncertain in climate models.

Quantifying nitrous oxide fluxes on multiple spatial scales in the Upper Midwest, USA.

This study seeks to quantify the roles of soybean and corn plants and the cropland ecosystem in the regional N2O budget of the Upper Midwest, USA. The N2O flux was measured at three scales (plant, the soil-plant ecosystem, and region) using newly designed steady-state flow-through plant chambers, a flux-gradient micrometeorological tower, and continuous tall-tower observatories. Results indicate that the following. (1) N2O fluxes from unfertilized soybean (0.03 ± 0.05 nmol m(-2) s(-1)) and fertilized corn plants (-0.01 ± 0.04 nmol m(-2) s(-1)) were about one magnitude lower than N2O emissions from the soil-plant ecosystem (0.26 nmol m(-2) s(-1) for soybean and 0.95 nmol m(-2) s(-1) for corn), confirming that cropland N2O emissions were mainly from the soil. (2) Fertilization increased the corn plant flux for a short period (about 20 days), and late-season fertilization dramatically increased the soybean plant emissions. (3) The direct N2O emission from cropland accounted for less than 20 % of the regional flux, suggesting a significant influence by other sources and indirect emissions, in the regional N2O budget.

A flux-gradient system for simultaneous measurement of the CH4, CO2, and H2O fluxes at a lake-air interface.

Inland lakes play important roles in water and greenhouse gas cycling in the environment. This study aims to test the performance of a flux-gradient system for simultaneous measurement of the fluxes of water vapor, CO2, and CH4 at a lake-air interface. The concentration gradients over the water surface were measured with an analyzer based on the wavelength-scanned cavity ring-down spectroscopy technology, and the eddy diffusivity was measured with a sonic anemometer. Results of a zero-gradient test indicate a flux measurement precision of 4.8 W m(-2) for water vapor, 0.010 mg m(-2) s(-1) for CO2, and 0.029 µg m(-2) s(-1) for CH4. During the 620 day measurement period, 97%, 69%, and 67% of H2O, CO2, and CH4 hourly fluxes were higher in magnitude than the measurement precision, which confirms that the flux-gradient system had adequate precision for the measurement of the lake-air exchanges. This study illustrates four strengths of the flux-gradient method: (1) the ability to simultaneously measure the flux of H2O, CO2, and CH4; (2) negligibly small density corrections; (3) the ability to resolve small CH4 gradient and flux; and (4) continuous and noninvasive operation. The annual mean CH4 flux (1.8 g CH4 m(-2) year(-1)) at this hypereutrophic lake was close to the median value for inland lakes in the world (1.6 g CH4 m(-2) year(-1)). The system has adequate precision for CH4 flux for broad applications but requires further improvement to resolve small CO2 flux in many lakes.

The influence of plants on atmospheric methane in an agriculture-dominated landscape.

The primary objective of this study was to clarify the influence of crop plants on atmospheric methane (CH4) in an agriculture-dominated landscape in the Upper Midwest of the United States. Measurements were carried out at two contrasting scales. At the plant scale, CH4 fluxes from soybean and corn plants were measured with a laser-based plant chamber system. At the landscape scale, the land surface flux was estimated with a modified Bowen ratio technique using measurements made on a tall tower. The chamber data revealed a diurnal pattern for the plant CH4 flux: it was positive (an emission rate of 0.4±0.1 nmol m(-2) s(-1), average of soybean and corn, in reference to the unit ground area) during the day, and negative (an uptake rate of -0.8±0.8 nmol m(-2) s(-1)) during the night. At the landscape scale, the flux was estimated to be 14.8 nmol m(-2) s(-1) at night and highly uncertain during the day, but the available references and the flux estimates from the equilibrium methods suggested that the CH4 flux during the entire observation period was similar to the estimated nighttime flux. Thus, soybean and corn plants have a negligible role in the landscape-scale CH4 budget.

Dew water isotopic ratios and their relationships to ecosystem water pools and fluxes in a cropland and a grassland in China.

Dew formation has the potential to modulate the spatial and temporal variations of isotopic contents of atmospheric water vapor, oxygen and carbon dioxide. The goal of this paper is to improve our understanding of the isotopic interactions between dew water and ecosystem water pools and fluxes through two field experiments in a wheat/maize cropland and in a short steppe grassland in China. Measurements were made during 94 dew events of the D and (18)O compositions of dew, atmospheric vapor, leaf, xylem and soil water, and the whole ecosystem water flux. Our results demonstrate that the equilibrium fractionation played a dominant role over the kinetic fractionation in controlling the dew water isotopic compositions. A significant correlation between the isotopic compositions of leaf water and dew water suggests a large role of top-down exchange with atmospheric vapor controlling the leaf water turnover at night. According to the isotopic labeling, dew water consisted of a downward flux of water vapor from above the canopy (98%) and upward fluxes originated from soil evaporation and transpiration of the leaves in the lower canopy (2%).

Quantifying the contribution of evaporation from Lake Taihu to precipitation with an isotope-based method.

Moisture recycling plays a crucial role in regional hydrological budgets. The isotopic composition of precipitation has long been considered as a good tracer to investigate moisture recycling. This study quantifies the moisture recycling fractions (fr) in the Lake Taihu region using spatial variations of deuterium excess in precipitation (dP) and surface water vapour flux (dE). Results show that dP at a site downwind of the lake was higher than that at an upwind site, indicating the influence of lake moisture recycling. Spatial variations in dP after sub-cloud evaporation corrections were 2.3, 1.4 and 3.2 ‰, and dE values were 27.4, 32.3 and 31.4 ‰ for the first winter monsoon, the summer monsoon and the second winter monsoon, respectively. Moisture recycling fractions were 0.48 ± 0.13, 0.07 ± 0.03 and 0.38 ± 0.05 for the three monsoon periods, respectively. Both using the lake parameterization kinetic fractionation factors or neglecting sub-cloud evaporation would decrease fr, and the former has a larger influence on the fr calculation. The larger fr in the winter monsoon periods was mainly caused by lower specific humidity of airmasses but comparable moisture uptake along their trajectories compared to the summer monsoon period.