Caffeine as a chemical tracer for contamination of urban rivers

ABSTRACT The growing urbanization in urban centers have continuously contributed to the deterioration of water quality in rivers. The use of caffeine as a chemical tracer for anthropic activities is an approach to the environmental monitoring of urban waterbodies, as its use is limited to humans and less susceptible to sampling error comparing to other traditional parameters for anthropic pollution. To analyze the possibility of using caffeine as a water quality parameter, the anthropic influence over three watersheds (Atuba, Belem and Palmital) from the Greater Curitiba was observed over five sampling campaigns. The caffeine was analyzed by liquid chromatography tandem diode array detection, after the solid phase extraction. Traditional parameters of water quality monitoring, such as ammoniacal nitrogen, thermotolerant coliforms, biochemical oxygen demand and orthophosphate, were measured for comparison. The results indicate anthropic influence over the basins, probably due to the lack of infrastructure, thus leading to the assumption of untreated domestic wastewater being discharged. The most degraded river was the Belem river (caffeine concentration of 23.08 µg.L-1). Caffeine presented itself as an appropriate approach for environmental monitoring, presenting a good correlation with the traditional parameters, such as for thermotolerant coliforms (R = 0.7375).

摘要：城市中心地区日益加剧的城市化进程持续导致河流水质不断恶化。将咖啡因作为人类活动的化学示踪剂，是开展城市水体环境监测的可行手段：因其仅与人类活动相关，相较于其他传统人为污染监测参数，其受采样误差的影响更小。为分析将咖啡因作为水质参数的可行性，研究团队针对大库里蒂巴地区的阿图巴河（Atuba）、贝伦河（Belem）与帕尔米塔尔河（Palmital）共3个流域开展了5次采样监测，以探究人为活动对其的影响。样品经固相萃取（solid phase extraction）前处理后，采用液相色谱-串联二极管阵列检测（liquid chromatography tandem diode array detection）法对咖啡因进行定量分析。同时测定了氨氮（ammoniacal nitrogen）、耐热大肠菌群（thermotolerant coliforms）、生化需氧量（biochemical oxygen demand）及正磷酸盐（orthophosphate）等传统水质监测参数以作对照。研究结果表明，上述流域受到了显著的人为活动影响，推测其原因为基础设施不完善，进而导致未经处理的生活污水直接排入水体。水质退化最严重的河流为贝伦河，其咖啡因浓度达23.08 µg·L⁻¹。咖啡因可作为适用于城市水体环境监测的有效指标，其与传统监测参数（如耐热大肠菌群，相关系数R=0.7375）具有良好的相关性。