发表机构
New York University（纽约大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对城市空气质量源解析的逆问题，提出IASA框架，结合可识别性分析，实现源活动系数估计、轨迹重建与不确定性评估，在新德里PM2.5数据集上验证了方法的有效性。
AI 中文摘要
从稀疏的城市空气质量传感器进行源解析是一个逆问题，其受传感器布置、风驱动传输、背景变化和噪声的限制。已知或代理排放清单通过将未知源场限制为有限的候选组来使归因具有意义，但不能保证这些组可通过观测区分。我们用低维非负时间基表示时变源活动，并将基于清单的解析公式化为风条件滞后逆问题，其中每个源-基系数产生一个传感器-时间指纹。在投影出单独的低维背景空间后，相关对象为投影滞后响应矩阵$\tilde{H}_\boldsymbol{\u03a6}$：在所选基分辨率下的精确可识别性要求其满列秩，而抗噪声归因由其奇异值、系数可见性、背景吸收、成对相干性和射线距离控制。我们提出了考虑可识别性的源解析（IASA）框架，该框架估计非负源-基系数、重建活动轨迹，并报告不可区分源的不确定性和保守分组建议。我们在基于新德里政府PM$_{2.5}$和风速记录、监管传感器位置以及四个代理源组构建的平台上实例化该框架，并定义了恢复、歧义、风多样性、背景压力、传输误差、清单鲁棒性和残差充分性的受控与观测评估。IASA报告在声明的清单、传输、背景、滞后和噪声下可防御的归因分辨率，而非最详细的可能向量。
英文摘要
Source apportionment from sparse urban air-quality sensors is an inverse problem limited by sensor placement, wind-driven transport, background variation, and noise. Known or proxy emission inventories make attribution meaningful by restricting the unknown source field to a finite set of candidate groups, but do not guarantee those groups are distinguishable from the observations. We represent time-varying source activity with a low-dimensional nonnegative temporal basis and formulate inventory-based apportionment as a wind-conditioned lagged inverse problem in which each source--basis coefficient produces a sensor-time fingerprint. After projecting out a separate low-dimensional background space, the relevant object is the projected lagged response matrix $\widetilde H_Φ$: exact identifiability at the chosen basis resolution requires its full column rank, while noise-robust attribution is controlled by its singular values, coefficient visibility, background absorption, pairwise coherence, and ray distance. We propose an identifiability-aware apportionment (IASA) framework that estimates nonnegative source--basis coefficients, reconstructs activity trajectories, and reports uncertainty and conservative grouping recommendations for indistinguishable sources. We instantiate it on a New Delhi platform built from government PM$_{2.5}$ and wind records, regulatory sensor locations, and four proxy source groups, and define controlled and observed evaluations of recovery, ambiguity, wind diversity, background stress, transport error, inventory robustness, and residual adequacy. IASA reports the attribution resolution defensible under the declared inventories, transport, background, lag, and noise rather than the most detailed possible vector.
