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Time series of data corresponding to Evans et al. (2022) "Natural variability and expansion of the nitrogen deficit within the Eastern Tropical North Pacific Oxygen Deficient Zone", containing secondary quality controlled data of 8 cruises in the ETNP ODZ, seven of which on the 110 W line, as well as supplemental sediment core data and CalCOFI oxygen data for comparison. Intermediate data products generated by the code used for this paper are also included, and the code to generate these intermediate products as well as the final outputs has been uploaded onto a separate Zenodo repository, "ETNP_ODZ_time_series_code" at https://doi.org/10.5281/zenodo.6519316. More detailed information is available in the README, but should you have any questions, please reach out to Allan Devol or Natalya Evans.

The rapid development of advanced metering infrastructure provides a new data source—building electrical load profiles with high temporal resolution. Electric load profile characterization can generate useful information to enhance building energy modeling and provide metrics to represent patterns and variability of load profiles. Such characterizations can be used to identify changes to building electricity demand due to operations or faulty equipment and controls. In this study, we proposed a two-path approach to analyze high temporal resolution building electrical load profiles: (1) time-domain analysis and (2) frequency-domain analysis. The commonly adopted time-domain analysis can extract and quantify the distribution of key parameters characterizing load shape such as peak-base load ratio and morning rise time, while a frequency-domain analysis can identify major periodic fluctuations and quantify load variability. We implemented and evaluated both paths using whole-year 15-minute interval smart meter data of 188 commercial office building in Northern California. The results from these two paths are consistent with each other and complementary to represent full dynamics of load profiles. The time- and frequency-domain analyses can be used to enhance building energy modeling by: (1) providing more realistic assumptions about building operation schedules, and (2) validating the simulated electric load profiles using the developed variability metrics against the real building load data.

Agricultural adaptation to climate change is critical for ensuring future food security. Social capital is important for climate change adaptation, but institutions and social networks at multiple scales (e.g., household, community, and institution) have been overlooked in studying agricultural climate change adaptation. We combine data from 13 sites in 11 low-income countries in East Africa, West Africa, and South Asia to explore how multiple scales of social capital relate to household food security outcomes among smallholder farmers. Using social network theory, we define three community organizational social network types (fragmented defined by lack of coordination, brokered defined as having a strong central actor, or shared defined by high coordination) and examine household social capital through group memberships. We find community and household social capital are positively related, with higher household group membership more likely in brokered and shared networks. Household group membership is associated with more than a 10% reduction in average months of food insecurity, an effect moderated by community social network type. In communities with fragmented and shared organizational networks, additional household group memberships is associated with consistent decreases in food insecurity, in some cases up to two months; whereas in brokered networks, reductions in food insecurity are only associated with membership in credit groups. These effects are confirmed by hierarchical random effects models, which control for demographic factors. This suggests that multiple scales of social capital—both within and outside the household—are correlated with household food security. This social capital may both be bridging (across groups) and bonding (within groups) with different implications for how social capital structure affects food security. Efforts to improve food security could recognize the potential for both household and community level social networks and collaboration, which further research can capture by analyzing multiple scales of social capital data.

A new concept, called synchronized Lissajous curve , is proposed to study synchro-waveform measurements that are obtained from waveform measurement units (WMUs) in power distribution systems. Two types of synchronized Lissajous curves are discussed. The first one is created by plotting the difference of two synchronized voltage waveforms versus the difference of two synchronized current waveforms. The second one is created by plotting the difference of two synchronized voltage waveforms versus the difference of the derivatives of two synchronized current waveforms. These two curves carry valuable and complementary information about the state of the power system and the root cause of events and disturbances. We characterize the area , rotational angle , and shape of the introduced synchronized Lissajous curves to study events and disturbances in power distribution feeders.

A new state estimation method is proposed for power distribution networks that suffer from low-observability. The proposed distribution system state estimation (DSSE) method leverages the high reporting rate of only a small number of distribution-level phasor measurement units (D-PMUs), a.k.a., micro-PMUs, to unmask and characterize sparsity among the state variables. The DSSE problem is formulated over differential synchrophasors as an adaptive group sparse recovery problem to track the changes that are made in the states of the system due to the events that are captured in D-PMU measurements. The formulated DSSE is further augmented to use adequate side information on the support of the vector of unknowns that is obtained from the outcome of an event-zone identification analysis prior to solving the DSSE problem. The sufficient conditions for the uniqueness of the obtained sparse recovery solution are derived with respect to the available side information. Moreover, a calibration mechanism is developed to address drifting in the tracking state estimation to enhance robustness.

Micro- and nanoscale patterned monolayers of plasmonic nanoparticles were fabricated by combining concepts from colloidal chemistry, self-assembly, and subtractive soft lithography. Leveraging chemical interactions between the capping ligands of pre-synthesized gold colloids and a polydimethylsiloxane stamp, we demonstrated patterning gold nanoparticles over centimeter-scale areas with a variety of micro- and nanoscale geometries, including islands, lines, and chiral structures (e.g., square spirals). By successfully achieving nanoscale manipulation over a wide range of substrates and patterns, we establish a powerful and straightforward strategy, nanoparticle chemical lift-off lithography (NP-CLL), for the economical and scalable fabrication of functional plasmonic materials with colloidal nanoparticles as building blocks, offering a transformative solution for designing next-generation plasmonic technologies.

Overview This dataset contains the following data and results: (1) National age-adjusted and age-invariant VSL estimates in 2019. (2) Global, regional, income-group, and national source-specific fractional contributions to PM2.5, health costs, and disparities estimated using both MR-BRT and GEMM CRFs, along with classifications of each country into different regions and income groups. (3) National total deaths and health costs, as well as source-specific deaths and costs using both MR-BRT and GEMM CRFs. (4) Gridded fractional source contributions to health costs from ambient PM2.5. The methodology and analysis of these results are described in the following article: Hao Yin, Erin McDuffie, Randall Martin, Michael Brauer, Global Health Costs of Ambient PM2.5 from Combustion Sources: A Modelling Study Supporting Air Pollution Control Strategies. Lancet Planetary Health. https://doi.org/10.1016/S2542-5196(24)00098-6 Data File Descriptions File “1 - Age-adjusted and age-invariant VSL.xlsx” documents age-adjusted and age-invariant VSL estimates for 204 countries in 2019. File “2 - Source-specific fractional contributions to PM2.5, health costs, and disparities.xlsx” summarizes global, regional, income-group, and national source-specific fractional contributions to PM2.5, health costs, and disparities estimated using both MR-BRT and GEMM CRFs, along with classifications of each country into different regions and income groups. File “3 - National total and source-specific deaths and health costs.xlsx” includes national total deaths and health costs, as well as source-specific deaths and costs using both MR-BRT and GEMM CRFs. The raster files for gridded fractional source contributions to health costs from ambient PM2.5 include multiple layers. The first raster file, "4-gridded_health_cost_fuel_vsl_age_contribution.tif" documents the gridded individual source contribution to health costs estimated using the age-adjusted VSL measure at a resolution of 0.01°x0.01°. The second raster file, "5-gridded_health_cost_fuel_vsl_contribution.tif" documents the gridded individual source contribution to health costs estimated using the age-invariant VSL measure at the same resolution. The layer names in these files (#4 and #5) are as follows: (1) Base (all sources included) (2) Solid biofuel (3) Total coal (4) Liquid fuel and natural gas (5) Windblown dust (6) AFCID (AFCID dust) (7) Agricultural waste burning (8) Other fires (9) Other sources The raster file "6-gridded_health_cost_comb_noncomb_vsl_age_contribution.tif " documents the gridded contributions of combustion and non-combustion sources to health costs from PM2.5 using the age-adjusted VSL measure. The raster file "7-gridded_health_cost_comb_noncomb_vsl_contribution.tif" documents the gridded contributions of combustion and non-combustion sources to health costs from PM2.5 using the age-invariant VSL measure. These two raster files (#6 and #7) contain three layers: (1) Base (all sources included) (2) Combustion sources (3) Non-combustion sources For more detailed information on the source definition, please refer to our paper. Hao Yin, Erin McDuffie, Randall Martin, Michael Brauer, Global Health Costs of Ambient PM2.5 from Combustion Sources: A Modelling Study Supporting Air Pollution Control Strategies. Lancet Planetary Health. https://doi.org/10.1016/S2542-5196(24)00098-6

Abstract Background Environmental factors such as temperature, rainfall, and vegetation cover play a critical role in malaria transmission. However, quantifying the relationships between environmental factors and measures of disease burden relevant for public health can be complex as effects are often non-linear and subject to temporal lags between when changes in environmental factors lead to changes in malaria incidence. The study investigated the effect of environmental covariates on malaria incidence in high transmission settings of Uganda. Methods This study leveraged data from seven malaria reference centres (MRCs) located in high transmission settings of Uganda over a 24-month period. Estimates of monthly malaria incidence (MI) were derived from MRCs’ catchment areas. Environmental data including monthly temperature, rainfall, and normalized difference vegetation index (NDVI) were obtained from remote sensing sources. A distributed lag nonlinear model was used to investigate the effect of environmental covariates on malaria incidence. Results Overall, the median (range) monthly temperature was 30 °C (26–47), rainfall 133.0 mm (3.0–247), NDVI 0.66 (0.24–0.80) and MI was 790 per 1000 person-years (73–3973). Temperature of 35 °C was significantly associated with malaria incidence compared to the median observed temperature (30 °C) at month lag 2 (IRR: 2.00, 95% CI: 1.42–2.83) and the increased cumulative IRR of malaria at month lags 1–4, with the highest cumulative IRR of 8.16 (95% CI: 3.41–20.26) at lag-month 4. Rainfall of 200 mm significantly increased IRR of malaria compared to the median observed rainfall (133 mm) at lag-month 0 (IRR: 1.24, 95% CI: 1.01–1.52) and the increased cumulative IRR of malaria at month lags 1–4, with the highest cumulative IRR of 1.99(95% CI: 1.22–2.27) at lag-month 4. Average NVDI of 0.72 significantly increased the cumulative IRR of malaria compared to the median observed NDVI (0.66) at month lags 2–4, with the highest cumulative IRR of 1.57(95% CI: 1.09–2.25) at lag-month 4. Conclusions In high-malaria transmission settings, high values of environmental covariates were associated with increased cumulative IRR of malaria, with IRR peaks at variable lag times. The complex associations identified are valuable for designing strategies for early warning, prevention, and control of seasonal malaria surges and epidemics.