The high-resolution spatio-temporal monitoring through low-cost sensors (LCS) provides important insights into the dynamics of localized pollution patterns and in conceptualizing effective environmental management and public health interventions. We assessed the role of meteorology and local sources on spatially distributed PM_2.5 pollutants measured from a unique urban-rural scaled network of LCS in Lucknow, India. The city-wide average PM_2.5 annual cycle ranged between 29 ± 7 μg/m³ during the monsoon to 64 ± 30 μg/m³ during the rest of the year. During non-monsoon seasons, >60% of air mass trajectories indicated regional level transportations primarily from the north-western plains of the Indo-Gangetic basin, with peak weighted concentration weighted trajectory (WCWT, scale: 0–100) value estimated at >70. To analyze the impact of local sources, we designed a statistical classification method to divide each seasonal distribution into five incremental concentration groups ranging from no risk zone to hotspots. The hotspots were identified both within urban and rural regions with their average concentration measured as 23 μg/m³ and 26 μg/m³ higher than the rest of the regions in pre-monsoon (2 hotspots) and post-monsoon (4 hotspots) season, respectively. Further analysis shows that the nighttime concentrations were much higher in several locations (up to 40% in pre-monsoon and 54% in post-monsoon compared to their daytime levels), indicating the greater impact of local sources in the presence of low boundary layer height. A diurnal trend analysis along with conditional bivariate probability function (CBPF) was performed to interpret the characteristics and locations of the dominant sources. The study highlights the importance of a dense network of LCS to scale air quality monitoring in spatially heterogeneous environments and as a futuristic tool for PM_2.5 exposure-based studies.