The urban areas of Pakistan exhibit some of the world’s highest levels of air pollution, primarily due to sub-2.5 μm particulate emissions. This issue significantly impairs both the country’s economy and the quality of life of its residents. Road transport is a significant contributor to anthropogenic air pollution but there are discrepancies about the extent of its share. Source apportionment and sectoral inventory studies attribute anywhere between 5 and >80% of the total air pollution to vehicular sources. This uncertainty propagates into the transport policy interventions that are informed by such studies and can thus hinder the achievement of desired pollution mitigation targets. In an effort to reconcile such discrepancies and guide future studies and policy-making efforts, this paper critically reviews source apportionment studies conducted in the urban centres of Pakistan over the past two decades. The strengths and weaknesses of different approaches are compared, and results from the studies are discussed based on the emissions profile of Pakistan’s automotive fleet that emerges. Inconsistencies in the reporting of pollutant concentrations and interpreting their impacts without accounting for the relative disease burden of different pollutant species are found to be the major reasons for the large variations in the reported sectoral shares. At the end, a framework for regular air pollution monitoring and source tracking is proposed in which high-fidelity receptor-based studies inform lower-fidelity but economical sectoral inventory assessments.

One aim of roadside green infrastructure (GI) is to mitigate exposure to local, traffic-generated pollutants. Here, we determine the efficacy of roadside GI in improving local air quality through the deposition and/or dispersion of airborne particulate matter (PM). PM was collected on both pumped air filters and on the leaves of a recently installed 'tredge' (trees managed as a head-high hedge) at an open road environment next to a primary school in Manchester, U.K. The magnetic properties of PM deposited on leaves and filters (size fractions PM₁₀ and PM_2.5) were deduced from hysteresis loops, first-order reversal curves (FORCs), and low-temperature remanence measurements. These were complemented with electron microscopy to identify changes in magnetic PM concentration downwind of the tredge/GI. We show that the tredge is permeable to airflow using a simple CO₂ tracer experiment; hence, it allows interception and subsequent deposition of PM on its leaves. Magnetic loadings per m³ of air from filters (PM₁₀ saturation magnetisation, M_s, at 5 K) were reduced by 40 % behind the tredge and a further 63 % in the playground; a total reduction of 78 % compared to roadside air. For the PM_2.5 fraction, the reduction in magnetic loading behind the tredge was remarkable (82 %), reflecting efficient diffusional capture of sub-5 nm Fe-oxide particles by the tredge. Some direct mixing of roadside and playground air occurs at the back of the playground, caused by air flow over, and/or through gaps in, the slowly-permeable tredge. The magnetic loading on tredge leaves increased over successive days, capturing ~23 % of local, traffic-derived PM₁₀. Using a heuristic two-dimensional turbulent mixing model, we assess the limited dispersion of PM < 22.5 μm induced by eddies in the tredge wake. This study demonstrates that PM deposition on leaves reduces exposure significantly in this school playground setting; hence, providing a cost-effective mitigation strategy.

We report the characterization of anthropogenic magnetic particulate matter (MPM) collected on leaves from roadside Callistemon (bottlebrush) trees from Lahore, Pakistan, and on known sources of traffic‐related particulates to assess the potential of first‐order reversal curve (FORC) diagrams to discriminate between different sources of anthropogenic magnetic particles. Magnetic measurements on leaves indicate the presence of surface‐oxidized magnetite spanning the superparamagnetic (<30 nm) to single domain (∼30–70 nm) to vortex size range (∼70–700 nm). Fe‐bearing particles are present both as discrete particles on the surface of larger mineral dust or carbonaceous particles and embedded within them, such that their aerodynamic sizes may be decoupled from their magnetic grain sizes. FORC diagrams of brake‐pad residue specimens show a distinct combination of narrow central ridge, extending from 0 to 200 mT, and a low‐coercivity, vertically spread signal, attributed to vortex and multi‐vortex behavior of metallic Fe. This is in agreement with scanning electron microscopy results that show the presence of metallic as well as oxidized Fe. Exhaust‐pipe residue samples display a more conventional “magnetite‐like” signal comprising a lower coercivity central ridge (0–80 mT) and a tri‐lobate signal attributed to vortex state and/or magnetostatic interactions. The FORC signatures of leaf samples combine aspects of both exhaust residue and brake‐pad endmembers, suggesting that FORC fingerprints have the potential to identify and quantify the relative contributions from exhaust and non‐exhaust (brake‐wear) emissions. Such measurements may provide a cost‐effective way to monitor the changing contribution; of future particulate emissions as the vehicle fleet is electrified over the coming years.

Particulate matter (PM) concentration levels in the London Underground (LU) are higher than London background levels and beyond World Health Organization (WHO) defined limits. Wheel, track, and brake abrasion are the primary sources of particulate matter, producing predominantly Fe-rich particles that make the LU microenvironment particularly well suited to study using environmental magnetism. Here we combine magnetic properties, high-resolution electron microscopy, and electron tomography to characterize the structure, chemistry, and morphometric properties of LU particles in three dimensions with nanoscale resolution. Our findings show that LU PM is dominated by 5–500 nm particles of maghemite, occurring as 0.1–2 μm aggregated clusters, skewing the size-fractioned concentration of PM artificially to larger sizes when measured with traditional monitors. Magnetic properties are largely independent of the PM filter size (PM₁₀, PM₄, and PM_2.5), and demonstrate the presence of superparamagnetic (< 30 nm), single-domain (30–70 nm), and vortex/pseudo-single domain (70–700 nm) signals only (i.e., no multi-domain particles > 1 µm). The oxidized nature of the particles suggests that PM exposure in the LU is dominated by resuspension of aged dust particles relative to freshly abraded, metallic particles from the wheel/track/brake system, suggesting that periodic removal of accumulated dust from underground tunnels might provide a cost-effective strategy for reducing exposure. The abundance of ultrafine particles identified here could have particularly adverse health impacts as their smaller size makes it possible to pass from lungs to the blood stream. Magnetic methods are shown to provide an accurate assessment of ultrafine PM characteristics, providing a robust route to monitoring, and potentially mitigating this hazard.

Public finance alone is insufficient to meet the scale of investment required for nature protection, restoration, and sustainable management. Mobilizing private capital is therefore essential, yet most nature-related projects are perceived as unbankable. They sit at the frontier of bankability: nascent and unproven initiatives that lack historical track records, face high execution risks, and generate uncertain or delayed revenues. This misalignment is compounded by investor behavior, as most financiers demand commercial rates of return, standardized transactions, and short payback periods. Even though investors differ in their appetite for risk, nature projects rarely meet these thresholds. Through a systematic review of literature across five ecosystems and three intervention types, this review identifies persistent barriers: limited financial returns, high risk, high transaction costs, and undervaluation of nature. We note that enabling conditions such as blended finance, risk-sharing instruments, and supportive regulation exist; however, we argue that reframing risk-return dynamics is critical to unlock scalable private finance for nature.

Identification of unknown micro‐ and nano‐sized mineral phases is commonly achieved by analyzing chemical maps generated from hyperspectral imaging data sets, particularly scanning electron microscope—energy dispersive X‐ray spectroscopy (SEM‐EDS). However, the accuracy and reliability of mineral identification are often limited by subjective human interpretation, non‐ideal sample preparation, and the presence of mixed chemical signals generated within the electron‐beam interaction volume. Machine learning has emerged as a powerful tool to overcome these problems. Here, we propose a machine‐learning approach to identify unknown phases and unmix their overlapped chemical signals. This approach leverages the guidance of Gaussian mixture modeling clustering fitted on an informative latent space of pixel‐wise elemental data points modeled using a neural network autoencoder, and unmixes the overlapped chemical signals of phases using non‐negative matrix factorization. We evaluate the reliability and the accuracy of the new approach using two SEM‐EDS data sets: a synthetic mixture sample and a real‐world particulate matter sample. In the former, the proposed approach successfully identifies all major phases and extracts background‐subtracted single‐phase chemical signals. The unmixed chemical spectra show an average similarity of 83.0% with the ground truth spectra. In the second case, the approach demonstrates the ability to identify potentially magnetic Fe‐bearing particles and their background‐subtracted chemical signals. We demonstrate a flexible and adaptable approach that brings a significant improvement to mineralogical and chemical analysis in a fully automated manner. The proposed analysis process has been built into a user‐friendly Python code with a graphical user interface for ease of use by general users.