Wildfire smoke impacts lake ecosystems.

Wildfire activity is increasing globally. The resulting smoke plumes can travel hundreds to thousands of kilometers, reflecting or scattering sunlight and depositing particles within ecosystems. Several key physical, chemical, and biological processes in lakes are controlled by factors affected by smoke. The spatial and temporal scales of lake exposure to smoke are extensive and under-recognized. We introduce the concept of the lake smoke-day, or the number of days any given lake is exposed to smoke in any given fire season, and quantify the total lake smoke-day exposure in North America from 2019 to 2021. Because smoke can be transported at continental to intercontinental scales, even regions that may not typically experience direct burning of landscapes by wildfire are at risk of smoke exposure. We found that 99.3% of North America was covered by smoke, affecting a total of 1,333,687 lakes ≥10 ha. An incredible 98.9% of lakes experienced at least 10 smoke-days a year, with 89.6% of lakes receiving over 30 lake smoke-days, and lakes in some regions experiencing up to 4 months of cumulative smoke-days. Herein we review the mechanisms through which smoke and ash can affect lakes by altering the amount and spectral composition of incoming solar radiation and depositing carbon, nutrients, or toxic compounds that could alter chemical conditions and impact biota. We develop a conceptual framework that synthesizes known and theoretical impacts of smoke on lakes to guide future research. Finally, we identify emerging research priorities that can help us better understand how lakes will be affected by smoke as wildfire activity increases due to climate change and other anthropogenic activities.

Tree-level almond yield estimation from high resolution aerial imagery with convolutional neural network.

Introduction: Estimating and understanding the yield variability within an individual field is critical for precision agriculture resource management of high value tree crops. Recent advancements in sensor technologies and machine learning make it possible to monitor orchards at very high spatial resolution and estimate yield at individual tree level. Methods: This study evaluates the potential of utilizing deep learning methods to predict tree-level almond yield with multi-spectral imagery. We focused on an almond orchard with the 'Independence' cultivar in California, where individual tree harvesting and yield monitoring was conducted for ~2,000 trees and summer aerial imagery at 30cm was acquired for four spectral bands in 2021. We developed a Convolutional Neural Network (CNN) model with a spatial attention module to take the multi-spectral reflectance imagery directly for almond fresh weight estimation at the tree level. Results: The deep learning model was shown to predict the tree level yield very well, with a R2 of 0.96 (±0.002) and Normalized Root Mean Square Error (NRMSE) of 6.6% (±0.2%), based on 5-fold cross validation. The CNN estimation captured well the patterns of yield variation between orchard rows, along the transects, and from tree to tree, when compared to the harvest data. The reflectance at the red edge band was found to play the most important role in the CNN yield estimation. Discussion: This study demonstrates the significant improvement of deep learning over traditional linear regression and machine learning methods for accurate and robust tree level yield estimation, highlighting the potential for data-driven site-specific resource management to ensure agriculture sustainability.

How Does Air Pollution Influence Housing Prices in the Bay Area?

In this paper we examine the effects of localized air pollution measurements on the housing prices in Oakland, CA. With high-resolution air pollution measurements for NO, NO2, and BC, we can assess the ambient air quality on a parcel-by-parcel basis within the study domain. We combine a spatial lag model with an instrumental variable method to consider both the spatial autocorrelation and endogeneity effects between housing prices and air pollution concentrations. To the best of our knowledge, this is the first work in this field that combines both spatial autocorrelation and endogeneity effects in one model with accurate air pollution concentration measurements for each individual parcel. We found a positive spatial autocorrelation with housing prices using Moral's I (value of 0.276) with the total sample number of 26,386. Somewhat surprisingly, we found a positive relationship between air pollution and housing prices. There are several possible explanations for this finding. Homeowners in high demand, low-stock housing areas, such as our study, may be insensitive to air pollution when the overall ambient air quality is relatively good. It is also possible that under clean air conditions, low variability in pollutant concentrations has little effect on property values. These hypotheses could be verified with more high-resolution air pollution measurements with a diversity of regions.

The pollution intensity of Australian power stations: a case study of the value of the National Pollutant Inventory (NPI).

This paper presents a comprehensive analysis of the pollutant emissions from electrical generation facilities reported to Australia's National Pollutant Inventory (NPI). The data, in terms of pollutant intensity with respect to generation capacity and fuel source, show significant variability. Based on reported data, the dominant pathway and environmental segment for emissions is point-source air emissions. Surprisingly, pollutant emissions from power stations are generally a very small fraction of Australia's facility and diffuse emissions, except for F, HCl, NO(x), PM2.5, SO2 and H2SO4 (where it constitutes between 30 and 90% of emissions). In general, natural gas and diesel facilities have higher organic pollutant intensities, while black and brown coal have higher metal/metalloid pollutant intensities and there is a wide variability for inorganic pollutant intensities. When examining pollutant intensities with respect to capacity, there is very little evidence to show that increased scale leads to more efficient operation or lower pollutant intensity. Another important finding is that the pollutant loads associated with transfers and reuse are substantial, and often represent most of the reported pollutants from a given generation facility. Finally, given the issues identified with the NPI data and its use, some possible improvements include the following: (i) linking site generation data to NPI data (especially generation data, i.e., MWh); (ii) better validation and documentation of emissions factors, especially the methods used to derive and report estimates to the NPI; (iii) using NPI data to undertake comparative life cycle impact assessment studies of different power stations and fuel/energy sources, or even intensive industrial regions (especially from a toxicity perspective) and (iv) linking NPI data in a given region to ongoing environmental monitoring, so that loads can be linked to concentrations for particular pollutants and the relevant guidelines (e.g., air, water, human health). Pollutant inventory systems are clearly valuable tools in understanding pollution burdens and ongoing analysis of the growing body of data should help to further improve environmental and public health outcomes. Overall, this study provides a valuable insight into the current status of pollutant intensities from Australia's electrical generation facilities and should be a valuable benchmark for future studies and international comparisons.