Airborne particulate matter (PM) is a complex mixture of particles thought to be associated with a range of adverse health effects, including female breast cancer. Current evidence on the association between PM and female breast cancer risk is inconsistent. This study investigated the association between long-term exposure to PM and breast cancer risk in a nested case-control study within the French E3N-Generation cohort including 5222 breast cancer cases identified over the 1990–2011 follow-up period and 5222 individually matched controls. Annual mean concentrations of PM10 and PM2.5 at participants’ residential addresses, were estimated using a land use regression model. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using conditional logistic regression models. ORs for each 10 µg/m3 increase in the average of PM2.5 and PM10 were 1.14 (95% CI: 0.99–1.30) and 1.08 (95% CI: 0.98–1.18), respectively. When restricted to invasive ductal and lobular carcinomas, ORs were 2.74 (95% CI: 1.05–7.15) for PM2.5 and 2.05 (95% CI: 1.11–3.78) for PM10. Comparable effects of PM exposure estimated by a chemistry transport model reinforces these findings. This study suggests a potential association between PM2.5 and PM10 exposure and breast cancer risk.
This study quantifies the impact of residential wood heating on winter air quality in France, including street-level analysis in Paris. It applies a multi-scale model to simulate regulated and emerging pollutants, such as organic matter (OM), black carbon (BC), and ultrafine particles (UFP), associated with health risks. Wood-burning emissions in Île-de-France and Paris were estimated using detailed local surveys, a new classification of appliances and emission factors accounting for condensable compounds. Over France, emissions were quantified from the EMEP top-down emission inventory, with post-estimated condensables. Wood burning is a major contributor to particulate pollution: in France, it accounts for 39.9% of PM2.5, 72.4% of BC, and 76.7% of OM. In Paris, contributions are similar, except for BC (27.2% at street level), influenced by other sources, as road traffic. Contributions to UFP are lower, ranging from 7% in Parisian streets to 15.5% over France. Wood burning significantly contributes to outdoor population exposure in Paris (33% for PM2.5, 20% for BC, and 70% for OM), with heating emissions mostly from auxiliary and comfort use (98%). Two 2030 scenarios were evaluated: business-as-usual (BAU) and a national emission-reduction objective. Under BAU, PM2.5 emissions and concentrations decline by 32.6% and 13.4% over France, and by 18.1% and 14.2% in Paris. Concentration reductions are smaller than emission reductions because some PM2.5 components (e.g. inorganics) are unaffected by wood-burning controls. The national objective scenario achieves larger impacts, typically 50%-70% greater than BAU, reducing PM2.5 concentrations of about 22%-24% in urban and street environments. Environmental Implications Health risks of fine particles depend on their composition and size, with black carbon, organics, and ultrafine particles emerging as key indicators. Our results indicate that reducing residential wood heating is an effective policy lever to mitigate wintertime particulate pollution in urban areas. In Paris, a large share of emissions arises from auxiliary and comfort heating, suggesting that targeted measures addressing non-essential wood use could deliver substantial air-quality benefits. The transition to newer heating technologies should be carefully evaluated to ensure that improvements in mass-based air quality are not offset by increased ultrafine particle emissions, which are not covered by current regulations but may have important health implications.
Using input-output analysis of EU FIGARO data (2010-2021), we calculate the embodied –i.e., supply chain-related– greenhouse gas emissions of digital industries (hardware, IT services, and communications). We show that the embodied emissions of demand for digital industries in 2021 are 4.1% of global emissions, with 77-87% occurring upstream (Scope 3). We also show that 42% of direct emissions from digital industries are transferred via supply chains and ultimately “hidden” in the embodied emissions of non-digital industries. Hardware contributes the largest share to global embodied emissions of digital industries, while the increasing demand for IT services drives emissions growth in the past decade. Our findings highlight the need to reduce digital emissions along all industries’ value chains, by considering embedded digital inputs, following circular economy principles for hardware manufacturing, and limiting embodied emissions associated with IT services, such as artificial intelligence services.
Greenhouse gas (GHG) emissions from land use and land-use change (LULUC) are major contributors to the climate change impact of agricultural products. The widely used method recommended by PAS 2050 when the previous land use is unknown has several limitations. The aim of this study was to develop a method to estimate GHG emissions from both direct land-use change (LUC) and land management changes (LMC), to be implemented in the French agricultural and food life cycle inventory database Agribalyse. The proposed method uses 50 m × 50 m spatially explicit land conversion data at the departmental scale with a shared-responsibility approach and regionalised carbon (C) stocks, in line with recent advances in LULUC accounting. It also includes GHG emissions associated with changes in hedgerow area and CO2 removals by the soil and biomass. We calculated reference values for five agricultural land-use categories (field crops and temporary grassland, vegetables and flowers, permanent grassland, vineyards, and orchards) in 94 departments of metropolitan France and mean national results for 26 agricultural products. Total net GHG emissions from LULUC at the national scale were calculated for the aggregate land-use category cropland per previous land-use category: cropland, grassland, forest, settlement, hedgerow, and others. Total net GHG emissions of LULUC from cropland in France in 2020 were equivalent to those estimated by the French National Emissions Inventory Agency, with a large contribution from grassland conversions, followed by forest and hedgerow conversions. Large CO2 removals by the soil were also estimated, associated mainly with LMC. GHG emissions per hectare varied widely among land-use categories and departments, ranging from − 2570 to 4969 kg CO2-eq∙ha−1∙year−1. For products assessed at the national scale, including LMC GHG emissions decreased total net GHG emissions per kilogramme without LULUC by 8–46
A new approach based on input–output (IO) analysis has emerged to estimate the carbon footprints of companies and their products from cradle to gate. While the approach relies on the same principles as the GHG Protocol, it uses a distributed iterative framework to improve the footprint estimations and reduce their uncertainty. While optimal estimations would result if all the world’s companies would enter such a system, this paper shows how such a distributed system could apply to the real world where many enterprises would stay out of the system. We show how the quality of the estimations with respect to the GHG Protocol would be increased by integrating scope 1 and scope 2 data from the value chains in the footprint estimations and progressively reducing the part of the remaining scope 3 data. To help with analyzing uncertainty, we show how to use the scope 1/2/3 decomposition to estimate the biases and the standard deviations of the computed production carbon intensities. We illustrate the model on macroeconomic data for 44 sectors and two regions (Europe and Rest of World), using the Inter-Country Input–Output database from the OECD. Such a system would necessarily rely on Information and Communication Technology, since the companies would be permanently interconnected in a large-scale meshed network, using an application protocol for data exchange.
